ArticlePDF Available

Predictive Effects of Structural Variation on Citation Counts

  • College of Computing and Informatics, Drexel University

Abstract and Figures

A critical part of a scientific activity is to discern how a new idea is related to what we know and what may become possible. As the number of new scientific publications arrives at a rate that rapidly outpaces our capacity of reading, analyzing, and synthesizing scientific knowledge, we need to augment ourselves with information that can guide us through the rapidly growing intellectual space effectively. In this article, we address a fundamental issue concerning with what information may serve as early signs of potentially valuable ideas. In particular, we are interested in information that is routinely available and derivable upon the publication of a scientific paper without assuming the availability of additional information such as its usage and citations. We propose a theoretical and computational model that predicts the potential of a scientific publication in terms of the degree to which it alters the intellectual structure of the state of the art. The structural variation approach focuses on the novel boundary-spanning connections introduced by a new article to the intellectual space. We validate the role of boundaryspanning in predicting future citations using three metrics of structural variation, namely, modularity change rate, cluster linkage, and centrality divergence, along with more commonly studied predictors of citations such as the number of co-authors, the number of cited references, and the number of pages. Main
Content may be subject to copyright.
Predictive Effects of Structural Variation on Citation
Chaomei Chen
College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104.
A critical part of a scientific activity is to discern how
a new idea is related to what we know and what may
become possible. As the number of new scientific pub-
lications arrives at a rate that rapidly outpaces our
capacity of reading, analyzing, and synthesizing scien-
tific knowledge, we need to augment ourselves with infor-
mation that can effectively guide us through the rapidly
growing intellectual space. In this article, we address a
fundamental issue concerning what kinds of informa-
tion may serve as early signs of potentially valuable
ideas. In particular, we are interested in information that
is routinely available and derivable upon the publication
of a scientific paper without assuming the availability
of additional information such as its usage and cita-
tions. We propose a theoretical and computational model
that predicts the potential of a scientific publication in
terms of the degree to which it alters the intellectual
structure of the state of the art. The structural varia-
tion approach focuses on the novel boundary-spanning
connections introduced by a new article to the intellec-
tual space. We validate the role of boundary-spanning in
predicting future citations using three metrics of struc-
tural variation—namely, modularity change rate, cluster
linkage, and Centrality Divergence—along with more
commonly studied predictors of citations such as the
number of coauthors, the number of cited references,
and the number of pages. Main effects of these factors
are estimated for five cases using zero-inflated negative
binomial regression models of citation counts. Key find-
ings indicate that (a) structural variations measured by
cluster linkage are a better predictor of citation counts
than are the more commonly studied variables such
as the number of references cited, (b) the number of
coauthors and the number of references are both good
predictors of global citation counts to a lesser extent,
and (c) the Centrality Divergence metric is potentially
valuable for detecting boundary-spanning activities at
interdisciplinary levels.The structural variation approach
offers a new way to monitor and discern the potential
of newly published papers in context. The boundary-
spanning mechanism offers a conceptually simplified
Received September 1, 2011; revised October 3, 2011; accepted October 5,
© 2011 ASIS&T Published online in Wiley Online Library
( DOI: 10.1002/asi.21694
and unifying explanation of the roles played by commonly
studied extrinsic properties of a publication in the study
of citation behavior.
A hallmark of scientific knowledge is its constant interplay
with new ideas proposed by the scientific community. New
ideas vary considerably in terms of how their value could be
perceived. Some are warmly embraced upon their conception
whereas others may go through lengthy periods of uncer-
tainty and controversy or even become totally ignored by
the scientific community. A critical part of scientific inquiry
is to discern where a new idea stands given what the sci-
entific community knows as a whole. This is a cognitively
demanding and conceptually challenging task. Not only do
we need to have an up-to-date understanding of the intel-
lectual structure of the relevant scientific fields but we also
must be able to identify exactly how a newly proposed idea
is connected to the intellectual structure. New scientific pub-
lications arrive faster than what any individual can possibly
read, analyze, and synthesize.
Detecting early signs of potentially valuable ideas has the-
oretical and practical implications. For instance, peer reviews
of new manuscripts and new grant proposals are under a grow-
ing pressure of accountability for safeguarding the integrity
of scientific knowledge and optimizing the allocation of
limited resources (Chubin, 1994; Chubin & Hackett, 1990;
Häyrynen, 2007; Hettich & Pazzani, 2006). Long-term strate-
gic science and technology policies require visionary think-
ing and evidence-based foresights into the future (Cuhls,
2001; Martin, 2010; Miles, 2010). In foresight exercises on
identifying future technology, experts’ opinions were found
to be overly optimistic with hindsight (Tichy, 2004). The
increasing specialization in today’s scientific community
makes it unrealistic to expect an expert to have a compre-
hensive body of knowledge concerning multiple key aspects
of a subject matter, especially in interdisciplinary research
© 2011 ASIS&T • Published online 14 November 2011 in Wiley Online
Library (wileyonlinelibrary. com). DOI: 10.1002/asi.21694
DOI: 10.1002/asi
The value, or perceived value, of an idea can be quanti-
fied in many ways. For example, the value of a good idea can
be measured by the number of people’s lives that it has saved,
the number of jobs that it has created, or the amount of rev-
enue that it has generated. In the intellectual world, the value
of a good idea can be measured by the number of other ideas
that it has inspired or the amount of attention that it has drawn.
In this article, we are concerned with identifying patterns and
properties of information that can tell us something about the
potential values of ideas expressed and embodied in scientific
publications. A citation count of a scientific publication is the
number of times other scientific publications have referenced
the publication. Using citations to guide the search for rele-
vant scientific ideas by way of association, known as citation
indexing, was pioneered by Eugene Garfield (1955). It is a
general consensus that citation behavior can be motivated
by both scientific and nonscientific reasons (Bornmann &
Daniel, 2006). Citation counts have been used as an indica-
tor of intellectual impact on subsequent research. There have
been debates over the nature of citations and whether all pos-
itive, negative, and self-citations should be treated equally.
Nevertheless, even a negative citation makes it clear that the
referenced work cannot be simply ignored.
What do we know about factors that may influence citation
counts in one way or another? One may address this ques-
tion from a few different points of view, largely depending
on where we draw our insights from: the past or the present.
An article that has been highly cited so far is likely to remain
highly cited according to the Matthew Effect (Merton, 1968).
An article that has been frequently downloaded or viewed
online is likely to become highly cited later (Brody & Harnad,
2005; Kurtz et al., 2005). Relying on direct evidence such as
visit counts, download counts, and citation counts that an
article already has obtained has relatively lower risks than
does making assessments based on indirect evidence. The
downside of such approaches is that the analysis is not pos-
sible until a sufficient period of time elapses from the time
of publication so that the article has a reasonable exposure to
the scientific community.A much longer delay is expected to
conduct a citation analysis because of the longer life cycle of
scholarly publication.
Researchers have searched for other clues that may inform
us about the potential impact of a newly published scientific
paper, especially ones that can be readily extracted from rou-
tinely available information at the time of publication instead
of waiting for download and citation patterns to build up over
time. Factors such as the track record of authors, the pres-
tige of authors’ institutions, and the prestige of the journal in
which an article is published are among the most promis-
ing clues that can provide an assurance of the quality of
the article, to an extent (Boyack, Klavans, Ingwersen, &
Larsen, 2005; Hirsch, 2007; Kostoff, 2007; van Dalen &
Kenkens, 2005; Walters, 2006). The common assumption
central to approaches in this category is that great researchers
tend to continuously deliver great work, and along a simi-
lar vein, an article published in a high-impact journal also
is likely to be of high quality itself. On one hand, these
approaches avoid the reliance on data that may not be read-
ily available upon the publication of an article and thus free
analysts from constraints due to the lack of download and cita-
tion data. On the other hand, the sources of information used
in these approaches are indirect to the new ideas reported
in scientific publications. In an analogy, we give credit to
an individual based on his or her credit history instead of
directly assessing the risk of the current transaction. In such
approaches, we will not be able to know precisely where the
novelty of an idea is from or whether similar ideas have been
proposed in the past.
The approach that we will introduce in this article aims to
provide specific trails of evidence to show why and how an
idea is novel with reference to the current intellectual struc-
ture of a scientific domain. We conceptualize the development
of scientific knowledge as a process of interplay between the
intellectual structure and a stream of incoming new ideas
conveyed in newly published scientific papers. Each new
idea may alter the current intellectual structure or leave the
structure intact. The prediction of the potential value, or the
impact, of an idea can be made computationally in terms
of the degree of structural change introduced by the idea.
In this context, we call this approach a structural variation
model. For example, if a new idea connects previously dis-
parate patches of knowledge, then its transformative potential
is higher than is the potential of ideas that are limited to well-
trodden paths over the existing structure. The central idea
of this approach is a boundary-spanning mechanism, which
is conceptualized as a production rule that drives the cita-
tion process. The intellectual structure can be represented
by networks of ideas. The conceptual change brought by
newly published scientific articles to the intellectual structure
can be quantified based on information that comes with the
publication of such articles, notably the authorship and cited
references. To validate that the structural variation model does
capture insightful information about the potential value of an
article, we investigate the extent to which structural variation
measures of articles predict their subsequent citations along
with other more commonly studied predictors such as the
number of coauthors and the number of cited references. The
structural variation model offers a conceptually simple and
unifying explanation of several commonly identified citation
predictors. For example, review and survey articles are often
highly cited. An explanation in our model is that they tend
to synthesize individual areas in a broader context than do
original research articles and they are more likely to include
boundary-spanning connections, which could be both inten-
tional and unintentional when a large number of topics are
reviewed. The number of coauthors has been recognized as a
potential factor for predicting high citation counts. A possible
explanation could be that multiple coauthors bring different
areas of expertise, and as a result, boundary spanning takes
place as they collaborate.
In the following sections, we will describe the procedure
of constructing baseline representations of the intellectual
structure and define structural variation metrics. Then, we
will validate the role of structural variation mechanisms
DOI: 10.1002/asi
in predicting subsequent citations through a series of gen-
eralized linear models, which are particularly suitable for
modeling count data with overdispersion and excessive zeros.
These concepts will be discussed later. We also will demon-
strate how the structural variation model provides a new
way to interact and explore specific connections that make
newly published scientific articles novel in the backdrop of
the current intellectual structure.
Structural Variation Model
There is a recurring theme from a diverse body of work on
creativity. A major form of creative work is to bridge previ-
ously disjoint bodies of knowledge. Notable studies include
the work of Ronald S. Burt (2004) in sociology and Don-
ald Swanson (1986a) in information science, and conceptual
blending as a theoretical framework for exploring human
information integration (Fauconnier & Turner, 1998). We
have been developing an explanatory and computational the-
ory of transformative discovery based on criteria derived from
structural and temporal properties (Chen, 2011; Chen et al.,
In the history of science, there are many examples of how
new theories revolutionized the contemporary knowledge
structure. For example, the 2005 Nobel Prize in medicine
was awarded to the discovery of Helicobacter pylori, a bac-
terium which was not believed to be possible to find in a
human gastric system (Chen et al., 2009). In literature-based
discovery, Swanson (1986a) discovered previously unnoticed
linkage between fish oil and Reynaud’s syndrome. In terror-
ism research, before the September 11, 2001 terrorist attacks,
it was widely believed that only those who directly witness
a traumatic scene or directly experience a trauma could have
the risk of posttraumatic stress disorder (PTSD); however,
later research has shown that people may develop PTSD
syndromes by simply watching the coverage of a traumatic
scene on television (Chen, 2006). In drug discovery, one of the
major challenges is to find new compound structures effective
in the vast chemical space that satisfy an array of constraints
(Lipinski & Hopkins, 2004). In mapping scientific frontiers
(Chen, 2003) and studies in science of science (Price, 1965),
it would be particularly valuable if scientists, funding agen-
cies, and policy makers could have tools that may assist them
to assess the novelty of ideas in terms of their conceptual
distance from the contemporary domain knowledge. In these
and many more scenarios, a common challenge for coping
with a constantly changing environment is to estimate the
extent to which the structure of a network should be updated
in response to newly available information.
Many studies have addressed factors that could explain
or even predict future citations of a scientific publication
(Aksnes, 2003; Hirsch, 2007; Levitt & Thelwall, 2008;
Persson, 2010). For example, is a paper’s citation count last
year a good predictor for new citations this year? Are the
download times a good predictor of citations? Is it true that the
more references a paper cites, the more citations it will receive
later? Similarly, the potential role of prestige, or the Matthew
Effect has been commonly investigated, ranging from the
prestige of authors to the prestige of journals in which articles
are published (Dewett & Denisi, 2004). However, many of
these factors are loosely and indirectly coupled with the con-
ceptual and semantic nature of the underlying subject matter
of concern. We refer to them as extrinsic factors in this article.
In contrast, intrinsic factors have direct and profound connec-
tions with the intellectual content and structure. One example
of an intrinsic factor is concerned with the structural variation
of a field of study.A notable example is the work by Swanson
(1986a) on linking previously disjoint bodies of knowl-
edge, such as the connection between fish oil and Reynaud’s
Extrinsic Factors of Citations
Bornmann and Daniel (2006) reviewed 30 studies of citing
behavior published from the 1960s to mid-2005. The general
tendency they found in the empirical studies is that citing
behavior is motivated by many scientific and nonscientific
factors. Their review summarized the status of numerous
detailed questions studied in the past. Note that some of
the most highly cited articles are review articles or articles
about methodology or tools (Tijssen,Visser, & van Leeuwen,
2002). Several studies have confirmed that the number of
references cited by a paper appears to be a good predictor
of its future citations, among other factors such as the pres-
tige of the paper’s author(s) and its journal (Walters, 2006).
For instance, review articles usually cite a large number of
references and tend to be highly cited (Aksnes, 2003). On
the other hand, the number of references cited by an article
alone is likely to be a poor proxy of the intellectual value of the
article. It is reasonable to argue that which references an arti-
cle cites matters more than how many references it cites. For
example, if an article Acites considerably more references
than does another Article B, then there are a few plausible
Article Aaddresses a topic much more extensively than does
Article B.
Article A, for example, a review paper, addresses more topics
than does Article B.
Article A, for example, a groundbreaking research paper,
addresses a topic that builds on a synthesis of multiple topics
whereas Article Bsynthesizes a less number of topics.
The list can go on. The value of our approach is to take
into account structural variations introduced by an article to
provide additional insights into the relationship between an
article and the state of the art.
Boyack et al. (2005) reported a method for predicting
the importance of current papers based on journal impor-
tance, reference importance, and author reputation. Measures
of importance in their study were based on citations to
780,049 papers published in 2002, with a window of citation
from the beginning of 2002 till the end of 2003. Forty-
eight percent of papers were never cited in this window.
The journal importance was calculated using the formula
published by the Institute for Scientific Information (ISI).
DOI: 10.1002/asi
The reference importance was calculated as the number of
times the reference in question was cited by papers pub-
lished in 2002. The author reputation was calculated as the
frequency of appearances of author–journal pairs in a 4-year
window before 2002. According to their report, a regression
analysis of logarithmic-transformed variables accounted for
approximately 30% of variance.Among the three importance
variables, the journal impact had the strongest correlation.
They suggested that properties such as these importance
factors can be used to rank articles without the need to
wait for citations to accumulate. While the ranking proce-
dure that they suggested is useful, their procedure implies
an assumption that articles citing more popular references
may get more citations. In this article, we take a different
approach that focuses specifically on how the contemporary
intellectual structure may change as a result of the way a
new article links references. In other words, the argument
that articles get more citations because they cite popular
references only reveals part of the story. Is it possible that
frequent citations to an article are not because of the popu-
larity of references it cites but rather where these references
are located in the intellectual structure of knowledge? Fur-
thermore, is it possible to measure the value of a scientific
contribution in terms of how many new ways of thinking it
may introduce? This is indeed a major motivating question of
our study.
Walters (2006) studied citations received by 428 articles
published in 12 crime-psychology journals in a single year of
2003 and identified nine major predictors of citations, includ-
ing author characteristics such as gender and citations to first
authors’ publications 2 years prior to the new publication;
article characteristics such as coauthors, article length, and
subject matter; and journal characteristics such as journal
impact. Walters’ study used negative binomial regression of
the citations received by these 428 articles 1 or 2 years after
their publication. His study suggested that author impact may
be a more powerful predictor of citations than is the impact
of a journal.
Kostoff (2007) compared highly and poorly cited research
articles published in The Lancet and found that the most cited
articles tend to have more coauthors, cite more references, and
have a longer abstract and more pages. Highly cited papers
tend to report clinical trials of much larger sample sizes than
do poorly cited papers. In a specific context, the h-index was
found to have a strong correlation with the number of cita-
tions: The h-index calculated based on the first 12 years in
a sample dataset has a correlation coefficient of 0.60 with
citations in the second 12 years (Hirsch, 2007).
Levitt and Thelwall (2008) studied highly cited articles in
six subject areas and found that predicting citation rank-
ing of highly cited articles using the subtotals of citations
in Years 5 and 6 is more accurate than is using the total
of citations in the first 6 years. Skilton (2009) found that
frequently cited coauthors and authors with diverse dis-
ciplinary backgrounds tend to be highly cited in natural
sciences. In contrast, the variety of disciplines represented
collectively by the authors of an article does not influence
citations. Persson (2010) addressed the question of whether
highly cited papers are more international (i.e., written by
coauthors from different geographic locations) than are less
highly cited ones. Based on data from four research areas,
three universities, four cities, and two countries, he con-
cluded that international papers dominate highly cited papers
from small countries, but are not well represented overall in
high-impact papers.
In addition to characteristics of authors and their institu-
tions, features of articles such as the length of the title, the
number of figures and tables, and the number and recency of
references and research methodology have been studied in
the literature (Haslam et al., 2008).
Intrinsic Factors of Citations
Researchers have made various attempts to characterize
future citations and identify emerging core articles (Shibata,
Kajikawa, & Matsushima, 2007; Walters, 2006). Shibata et
al. (2007) for example, studied citation networks in two
subject areas, Gallium Nitride and Complex Networks, and
found that while past citations are a good predictor of near-
future citations, the betweenness centrality is correlated with
citations in a longer term.
Upham, Rosenkopf, and Ungar (2010) studied the role of
cohesive intellectual communities—schools of thought—in
promoting and constraining knowledge creation. They ana-
lyzed publications on management, and concluded that it is
significantly beneficial for new knowledge to be a part of a
school of thought and that the most influential position within
a school of thought is in the semiperiphery of the school.
In particular, boundary-spanning research positioned at the
semiperiphery of a school would attract attention from other
schools of thought and receive the most citations overall.
Their study used a zero-inflated negative binomial regres-
sion (ZINB). Negative binomial regression models have been
used to predict the expected mean patent citations (Fleming &
Bromiley, 2000). Hsieh (2011) studied inventions as a com-
bination of technological features. In particular, the closeness
of features plays an interesting role. Neither overly related nor
loosely related features are good candidates for new inven-
tions. Useful inventions arise with rightly positioned features
where the cost of synthesis is minimized.
Takeda and Kajikawa (2010) reported three stages of clus-
tering in citation networks. In the first stage, core clusters
are formed, followed by the formation of peripheral clus-
ters and the continuous growth of the core clusters. Finally,
the core clusters’ growth again becomes predominant. Buter,
Noyons, and van Raan (2011) studied the emergence of an
interdisciplinary research area from fields that had not shown
interdisciplinary connections before. They used journal sub-
ject categories as a proxy for fields and citations as a measure
of interdisciplinary connection.
Lahiri, Maiya, Sulo, Habiba, and Wolf (2008) addressed
how structural changes of a network may influence the spread
of information over the network.Although they did not study
bibliographic networks, per se, their study has indicated that
DOI: 10.1002/asi
FIG. 1. An overview of the structural variation model.
predictions made about how information spreads over a net-
work are sensitive to structural changes of the network. This
observation underlines the importance of taking structural
change into account in the development of metrics based on
topological properties of networks.
Leydesdorff (2001, p. 146) raised questions that are
closely related to what we are addressing: “How does the
new text link up to the literature, and what is its impact
on the network of previously existing relations?” He took
a quite different approach and analyzed word occurrences
in scientific papers from an information-theoretic perspec-
tive. In his approach, the publication of a paper is perceived
as an event that may lead to the reduction of uncertainty
involved in the current state of knowledge. He devised
diagrams that depict pathways of how a particular paper
improves the efficiency of communication. Although the
information-theoretic approach and our structural variation
approach currently operate on different units of analysis
with distinct theoretical underpinnings, both share the fun-
damental concern of changes introduced by newly published
scientific papers on the existing body of knowledge.
As noted, many studies in the literature have addressed
factors that may influence citations. The value of our work
is the introduction of the structural variation paradigm along
with computational metrics that can be integrated into inter-
active exploration systems to better understand precisely the
impact of individual links made by a new article.
In this article, we conceptualize that structural variation
is an essential process that advances scientific knowledge.
The intellectual structure at a given point of time is subject
to structural changes introduced by newly published scien-
tific articles. The intellectual structure may be represented
in many different types of networks, including networks of
co-cited references, networks of co-cited authors, or net-
works of co-occurring keywords. Given a scientific article,
publications prior to the publication of the article form a
baseline representation of the intellectual structure at the spe-
cific time of publication. Structural variation metrics measure
the degree of structural change introduced by information
derived from the article. An overview of the procedure is
depicted in Figure 1. Individual steps are explained later in
corresponding sections.
DOI: 10.1002/asi
Data Collection
First, we choose a topic or a target field of study. In this
article, we include five cases drawn from four research areas:
terrorism, mass extinction, complex network analysis, and
knowledge domain visualization. We have studied some of
these research areas in our previous studies. Choosing a famil-
iar domain can help us to concentrate on new insights to be
revealed by our new approach. We also include a dataset of
articles defined by the journals in which they are published.
We used the Web of Science as the source of our data, although
the method is applicable to other sources of citation data, such
as Scopus and Google Scholar. In the rest of this article, bib-
liographic records were retrieved from the Web of Science
unless stated otherwise.
Given a chosen topic, a dataset can be constructed in two
ways. One way is to search for records of articles by rele-
vance, determined by matching terms and other attributes.
For instance, a dataset of bibliographic records on terrorism
research can be constructed by using a topic search for the
occurrence of the term terrorism in the title, the abstract,
or the keyword field of a record. It is possible to define a
dataset based on specific source journals, such as all articles
published in Nature and Science. The other way is to locate
articles by association formed by cited references. The sec-
ond method starts with a seed article. All articles that have
at least one overlapping reference with the seed article are
identified as related records. The global citation count of an
article is the total number of times the article is referenced
by other articles in the Web of Science at the time of retrieval.
The local citation count is the total number of times the arti-
cle is referenced by other articles within the retrieved dataset,
which is a subset of the entire collection of the Web of Science.
The global citation count is used in the analysis reported in
this article because we assume that it is a less biased proxy
of impact than is the local citation count.
A seed article is used as a starting point to reconstruct the
structural representations prior to and after the publication of
the seed article. We use a two-stage expansion process. First,
all the references cited by the seed articles are identified in
the Web of Science as a set CitedBy(s). Second, articles in the
Web of Science that cite at least one reference in CitedBy(s)
form a set SExpanded(s).
SExpanded(s Articles)
={aArticles|CitedBy(a) CitedBy(s) =∅}
The final set of articles serves as the representation of the
population of the original seed article. This process of
citation expansion can be repeated as many times as mean-
ingful to the purpose of the intended study; for example,
aSExpanded(s) SExpanded (a). A description of the process is
detailed in our earlier publication on forward expansion
(Chen, Lin, & Zhu, 2006). Although we do not rule out the
possibility that a relevant article does not have any overlap
with an expanded set of articles generated in this procedure
(e.g., the relevance is purely established at a semantic level),
in practice it would be rare to come across an article that is
relevant, but cites a totally different body of literature, espe-
cially when the body of literature is identified by the two-step
citation expansion. Such articles would either ignore the rele-
vant literature or completely innovative. Two cases are based
on seeded datasets—Complex Network Analysis and CiteS-
pace Expanded—whereas the other three cases are based on
the relevance of topic search.
To explore various configurations of the structural varia-
tion model, we apply the procedure to a number of datasets
representing a diverse range of topics and subject domains.
The background of each case is outlined, and additional
references are provided for further reading.
Complex Network Analysis
This dataset overlaps with the Small-World Networks dataset,
but the two datasets were constructed differently. A topic
search for “complex network analysis” revealed two most
frequently cited articles: Barabási and Albert (1999) and
Watts and Strogatz (1998).We constructed an updated dataset
with these two articles as seed articles. First, one seed arti-
cle was used to form a subdataset. Then, the two datasets
were merged to form a combined dataset on complex network
The seed article (Barabási & Albert, 1999) has the high-
est citation count. As of August 5, 2011, it has been cited
5,792 times in the Web of Science. The article by Watts
and Strogatz (1998) has the second-highest citation count
of 5,291. For each of the seed articles, we retrieved all
the records that share at least one cited reference with the
seed article. Then, we merged all the records and formed
a combined dataset. The seed Barabási_1999 has 8,919
related articles published between 1980 and 2011. If we limit
our search to the period of 1996 to 2004 and to original
research articles, reviews, and proceedings, 2,326 records
were retained in the subset. The seed article Watts_1998 has
14,393 related records. Interestingly, this is 1.6 times more
than what Barabási_1999 has, although Barabási_1999 has
500 more citations. It appears that Watts_1998 involves a
wider range of topics than does the Barabási_1999 article. The
merged dataset from the two subsets contains 6,764 records.
We first analyzed the dataset seeded by Barabási1999, then
the dataset seeded by Watts1998, and finally, the merged
Mass Extinctions research is not new. Some of its major
groundbreaking works appeared in early 1980s. On the other
hand, the field is as active as ever because many questions
remain concerning mass extinctions. Our previous study of
the evolution of mass extinctions research between 1981 and
2004 revealed a shift of research focus in early 1990s from
the K-T boundary mass extinction to the Permian extinction
(Chen, 2006; Chen, Cribbin, Macredie, & Morar, 2002). We
expect to detect considerable structural variations within the
range of 1991 to 2010.
The Mass Extinctions dataset contains 1,745 records of
journal articles, conference proceedings, and review arti-
cles published between 1991 and 2010 on mass extinctions.
Records were retrieved using a topic search query “mass
extinction” on April 2010. The dataset does not cover articles
published later in 2010, but this does not affect our analysis
because we take the exposure time into account in generalized
linear models.
DOI: 10.1002/asi
This dataset consists of bibliographic records that resulted
from a topic search in the Web of Science on terrorism between
1996 and 2005. A total of 1,303 articles were retrieved on
July 11, 2011. The citation counts of articles in the dataset
indicate the number of citations an article has received since
its publication up to July 2011. For example, an article pub-
lished in January 1996 would have the longest exposure time
of over 15 years whereas an article published in December
2005 would have the shortest exposure time of more than 5
years. The exposure time is taken into account in the construc-
tion of generalized linear models (discussed in more detail
In a previous study, we analyzed an earlier period of the
same subject (1990–2003) (Chen, 2006). Several conceptual
transformations were revealed by our previous study. In par-
ticular, we were able to identify a conceptual change from
the topological properties of a synthesized network of co-
cited references. Prior to the terrorist attacks on September
11, 2001, it was generally believed that PTSD would be
found only in people who had a direct experience of trauma.
Researchers then discovered after the extensive coverage of
the September 11 terrorist attacks on mass media that peo-
ple who were far away from New York also could develop
PTSD symptoms (Galea et al., 2002). This is one example of
boundary-spanning in that a new idea redefines the boundary
of a topic area and introduces fundamental changes to the orig-
inal intellectual structure. The terrorist attacks on September
11, 2001 occurred within the 1996 to 2005 range. Therefore,
we expect that the updated dataset would provide relevant
information for us to study the dynamics of the structural
Two cases are derived from this dataset with different con-
figurations of the length of time slices. We use both 2- and
3-year time slices to identify the procedural implications of
using time slices of different duration.
CiteSpace Expanded
The case is seeded by our 2006 article on CiteSpace (Chen,
2006). This dataset consists of papers that have at least one
cited reference in common with our seed article. Based on
the content of the seed article, we anticipate that there will be
three subareas involved in the dataset: scientometrics, mass
extinctions, and terrorism. CiteSpace is a scientometric tool
that has been applied to the analysis of mass extinctions and
Baseline Networks
The basic assumption in the structural variation approach
is that the extent of a departure from the current intellectual
structure is a necessary condition for a potentially transforma-
tive idea in science. In other words, a potentially transforma-
tive idea first needs to bring changes to the existing structure
of knowledge. To measure the degree of structural variation
introduced by a scientific article, the intellectual structure at
a particular moment of time needs to be represented in such a
way that structural changes can be computationally detected
and manually verifiable. Bibliographic networks can be com-
putationally derived from scientific publications. Research
in scientometrics and citation analysis routinely uses cita-
tion and co-citation networks as a proxy of the underlying
intellectual structure. In this article, we focus on using sev-
eral types of co-citation and co-occurrence networks as the
representation of a baseline network.
A network represents how a set of entities are connected.
Entities are represented as nodes, or vertices, in the network.
Their connections are represented as links, or edges. Relevant
entities in our context include several types of information
that can be computationally extracted from a scientific arti-
cle, such as references cited by the article, authors and their
affiliations, the journal in which the article is published, and
keywords in the article. In this article, we limit our discus-
sions to networks that are formed with a single type of entity,
although networks of multiple types of entities are worth con-
sidering once we establish a basic understanding of structural
variations in networks of a single type of entity.
Once the type of entities is chosen, the nature of the inter-
connectivity between entities is to be specified to form a
network. Networks of co-occurring entities represent a wide
variety of types of connectivity. A network of co-occurring
words represents how words are related in terms of whether
and how often they appear in the vicinity of each other. Co-
citation networks of entities such as references, authors, and
journals can be seen as a special case of co-occurring net-
works. For example, co-citation networks of references are
networks of references that appear together in the bodies of
scientific papers; these references are co-cited.
Networks of co-cited references represent more specific
information than do networks of co-cited authors because
references of different articles by the same author would be
lumped together in a network of co-cited authors. Similarly,
networks of co-cited references are more specific than are
networks of co-cited journals. We refer to such differences
in specificity as the granularity of networks. Measurements
of structural variation need to take the granularity factor into
account because it is reasonable to expect that networks at dif-
ferent levels of granularity would lead to different measures
of structural variations.
Another decision to be made about a baseline network is
a sampling issue. Taking a particular year as a standing point
to look at in the past, how far back should we consider in
the construction of a baseline network that would adequately
represent the underlying intellectual structure? Does the net-
work become more accurate if we go back more into the past?
Will it be more efficient if we limit it to the most recent years
that really matter the most? In this article, given articles pub-
lished in a particular year Y, the baseline network represents
the intellectual structure using information from articles pub-
lished up to year Y1. Two types of baseline networks are
investigated in this article: ones using a moving window of
a fixed size [Yk,Y1] and ones using the entire history
[Y0,Y1], where Y0is the earliest year of publication for
records in the given dataset.
Structural Variation Metrics
We expect that the degree of structural variation intro-
duced by a new article can offer prospective information
DOI: 10.1002/asi
because of the boundary-spanning mechanism. If an article
introduces novel links that span the boundaries of different
topics, then we expect that this signifies its potential in taking
the intellectual structure for a new turn.
Given a baseline network, structural variationscan be mea-
sured based on information provided by a particular article. In
this article, we introduce three metrics of structural variation.
Each metric quantifies the degree of change in the baseline
network introduced by information provided by an article.
No usage data are involved in the measurement. The three
metrics are modularity change rate, intercluster linkage, and
Centrality Divergence.The definitions of the first two metrics
depend on a partition of the baseline network, but the third one
does not. A partition of a network decomposes the network
into nonoverlapping groups of nodes. For example, clustering
algorithms such as spectral clustering can be used to partition
a network.
The theoretical underpinning of the structural variation
is that scientific discoveries, at least a subset of them, can
be explained in terms of boundary-spanning, brokerage, and
synthesis mechanisms in an intellectual space (Chen et al.,
2009). This conceptualization generalizes the principle of
literature-based discovery pioneered by Swanson (1986a,
1986b), which assumes that connections between previously
disparate bodies of knowledge are potentially valuable. In
Swanson’s famous ABC model, the relationships AB and
BC are known in the literature. The potential relationship
AC becomes a candidate that is subject to further scientific
investigation (Weeber, 2003). Our conceptualization is more
generic in several ways. First, in theABC model, theAC rela-
tion changes an indirect connection to a direct connection
whereas our structural variation model makes no assump-
tion about any prior relations. Second, in the ABC model,
the scope of consideration is limited to relationships involv-
ing three entities. In contrast, our structural variation model
takes a wider context into consideration and addresses the
novelty of a connection that links groups of entities as well as
connections linking individual entities. Because of the broad-
ened scope of consideration, it becomes possible to more
effectively search for candidate connections. In other words,
given a set of entities, the size of the search space of poten-
tial connections can be substantially reduced if additional
constraints are applicable for the selection of candidate con-
nections. For example, the structural hole theory developed
in social network analysis emphasizes the special potential of
nodes that are strategically positioned to form brokerage, or
boundary-spanning, links and create good ideas (Burt, 2004;
Chen et al., 2009).
Modularity Change Rate
Given a partition of a network (i.e., a configuration of
clusters), the modularity of the network measures the degree
of interconnectivity among the groups of nodes identified
by the partition. If different clusters are loosely connected,
then the overall modularity would be high. In contrast, if
clusters are interwoven, then the modularity would be low. We
follow Newman’s algorithm (Newman, 2006) to calculate the
modularity with reference to a cluster configuration generated
by spectral clustering (Chen, Ibekwe-SanJuan, & Hou, 2010;
Luxburg, 2006). Suppose the network Gis partitioned by a
partition Cinto kclusters such that G=c1+c2++ck,
Q(G) is defined as follows, where mis the total number of
edges in the network G, and nis the number of nodes in G.
j) is known as Kronecker’s delta. It is 1 if nodes niand
njbelong to the same cluster and 0 otherwise. deg(ni)isthe
degree of node ni. The range of Q(G) is between 1 and 1.
Q(G, C) =1
i,j =0[δ(ci,c
j)·(A]ij deg(ni)·deg(nj)
The modularity of a network is a measure of the overall struc-
ture of the network. Its range is between 1 and 1. The
modularity change rate (MCR) of a scientific paper measures
the relative structural change due to the information from the
published paper with reference to a baseline network. For
each article a, and a baseline network Gbaseline, we define the
MCR as follows:
MCR(a) =Q(Gbaseline,C)Q(GbaselineGa,C)
Q(Gbaseline,C) ·100
where Gbaseline Gais the updated baseline network by
information from the article a. For example, suppose refer-
ence nodes niand njare not connected in a baseline network
of co-cited references but they are co-cited by article a. A new
link between niand njwill be added to the baseline network.
In this way, the article changes the structure of the baseline
Intuitively, adding a new link anywhere in a network
should not increase the modularity of the network; it should
either reduce the modularity or leave it intact. However, the
change of modularity is not a monotonic function as we ini-
tially expected. In fact, it depends on where the new link is
added and how the network is structured. Adding a link may
reduce the proportion of the modularity in some clusters, but
it may increase the modularity in other clusters in the net-
work. Thus, the overall modularity change is not monotonic.
Diagrams in Figure 2 illustrate these scenarios.
Without losing any generality, assume that an article adds
one link at a time to a given baseline network. If the new
link connects two distinct clusters, then it has no effect on
the corresponding term in the updated modularity because
by definition, δij =0, and the corresponding term becomes 0.
Such a ink is illustrated by the dashed link e5,10 in the top dia-
gram in Figure 2. The new link, eij , will increase the degree
of nodes iand jby 1 (i.e., deg(i) will become deg(i) +1.
The total number of edges mwill increase to m+1. A simple
calculation at the bottom of Figure 2 shows that terms in the
modularity formula involving blue links will decrease from
their previous values. However, if the network has clusters
DOI: 10.1002/asi
FIG. 2. Scenarios that may increase or decrease individual terms in the modularity metric. [Color figure can be viewed in the online version, which is
available at]
such as CAwith no changes in node degrees, then the corre-
sponding values of terms of lines in red will increase from
their previous values as the denominator increases from 2mto
2(m+1). In summary, the updated modularity may increase
as well as decrease, depending on the structure of the net-
work and where the new link is added. With this particular
definition of modularity, between-cluster links are always
associated with a zero-valued term in the overall modular-
ity formula due to the Kronecker’s delta. What we see in the
change of modularity is a combination of results from several
scenarios that are indirectly affected by the newly added link.
Next, we will introduce our next metric to reflect the changes
in terms of between-cluster links.
As shown in Figure 3, two articles with the highest MCRs
in a small dataset of papers that cited Chen (2006) were
review articles. Review articles tend to cover many top-
ics, as one would expect, which would mean adding many
boundary-spanning co-citation connections to baseline net-
works and thus leading to a substantial structural variation
in terms of modularity. The two review papers have dif-
ferent distributions of boundary spanning links: one with
references concentrated on a small number of clusters in
recent years and the other with references traced back sev-
eral decades ago in distinct clusters. Both papers cited the
informetrics cluster (No. 11).
Cluster Linkage
The Cluster Linkage (CL) measures the overall structural
change introduced by an article ain terms of new connections
added between clusters. Its definition assumes a partition of
the network. We introduce a function of edges λ(ci,c
j) which
is the opposite of δij used in the modularity definition. The
value of λij is 1 for an edge across distinct clusters ciand
cj. It will be 0 for edges within a cluster. λij will allow us
to concentrate on between-cluster links and ignore within-
cluster links, which is the opposite of how the modularity
metric is defined. The new metric Linkage is the sum of all
DOI: 10.1002/asi
FIG. 3. The depth and breadth of novel connections made by two articles with the highest modularity change rate. Both are review articles. Barilan 2008
(left) cited references in recent years within a relatively small number of clusters whereas Morris 2008 (right) cited many earlier publications across a wider
range of clusters. [Color figure can be viewed in the online version, which is available at]
the weights of between-cluster links eij divided by K—the
total number of clusters in the network. Linking to itself is
not allowed (i.e., we assume eii =0 for all nodes. Using link
weights makes the metric sensitive to links that strengthen
connections between clusters in addition to novel links that
make unprecedented connections between clusters.
It is possible to take into account the size of clus-
ters that a link is connecting so that connections between
larger sized clusters become more prominent in the mea-
surement. For example, one option is to multiple each eij
by (size(ci) ·size(cj))/ max(size(cki)). In this article,
the metric is defined without such modifications for simplic-
ity. Suppose that Cis a partition of G; then the Linkage metric
is defined as follows:
Linkage(G, C) =n
i=jλij eij
λij =0,n
i∈ cj
The Cluster Linkage is defined as the difference of Link-
age before and after new between-clusters links added by an
article a.
CL(a) =Linkage(a)
=Linkage(Gbaseline Ga,C)Linkage(G baseline,C)
Linkage(G+G) is always greater than or equal to Link-
age(G). Thus, CL is nonnegative.
Centrality Divergence
The Centrality Divergence (CKL) metric measures the
structural variation caused by an article ain terms of the
divergence of the distribution of betweenness centrality
CB(vi) of nodes viin the baseline network. This defini-
tion does not involve any partitions of the network. If
nis the total number of nodes, the degree of structural
change CKL(G,a) can be defined in terms of the KL
CKL(Gbaseline ,a) =
For nodes where pi=0orqi=0, we reset them as a small
number 106to avoid log(0).
Statistical models. We constructed negative binomial (NB)
and ZINB models to validate the role of structural variation
in predicting future citation counts of scientific publica-
tions. The negative binomial distribution is generated by a
sequence of independent Bernoulli trials. Each trial is either
a “success” with a probability of por a “failure” with a
probability of (1 p). Here, the terminology of success and
failure in this context does not necessarily represent any prac-
tical preferences. The random number of successes Xbefore
DOI: 10.1002/asi
encountering a predefined number of failures rhas a negative
binomial distribution:
XNB(r, p).
One can adapt this definition to describe a wide variety of
count events. Citation counts belong to a type of count events
with an overdispersion (i.e., the variance >the mean). NB
models are commonly used in the literature to study this type
of count events. Two types of dispersion parameters are used
in the literature, θand α, where θ·α=1.
Zero-inflated count models are commonly used to account
for excessive zero counts (Hilbe, 2011; Lambert, 1992). Zero-
inflated models include two sources of zero citations: the
point mass at zero I{0}(y) and the count component with a
count distribution fcount(counts) such as negative binomial
or Poisson (Zeileis, Kleiber, & Jackman, 2011). The proba-
bility of observing a zero count is inflated with probability
π=fzero(zero citations).
ZINB models are increasingly used in the literature to
model excessive occurrences of zero citations (Fleming &
Bromiley, 2000; Upham et al., 2010). The report of a ZINB
model consists of two parts: the count model and the zero-
inflated model. One way to test whether a ZINB model is
superior to a corresponding NB model is known as the Vuong
test. The Vuong test is designed to test the null hypothesis that
the two models are indistinguishable. Akaike’s Information
Criterion (AIC) also is commonly used to evaluate the good-
ness of a model. Models with lower AIC scores are regarded
as better models in terms of the relative goodness of fit.
In this article, we focus on global citation counts of scien-
tific publications recorded in the Web of Science. NB models
are defined as follows using log as the link function.
Global citations Coauthors +Modularity Change Rate
+Cluster Linkage +Centrality Divergence
+References +Pages.
Global citations is the dependent variable. Coauthors is
a factor of three levels of 1, 2, and 3. Level 3 is assigned to
articles with three or more coauthors. Coauthors is an indi-
rect indicator of the extent to which an article synthesizes
ideas from different areas of expertise represented by each
Three structural variation metrics are included as covari-
ants in generalized linear models: MCR, CL, and CKL.
According to our theory of creativity, groundbreaking ideas
are expected to cause strong structural variations. If global
citation counts provide a reasonable proxy of recognitions
of intellectual contributions in a scientific community, we
would expect that at least some of the structural variation met-
rics will have statistically significant main effects on global
The number of cited references and the number of pages
are commonly reported in the literature as good predictors
of citations. To compare the effects of structural variation
with these commonly reported extrinsic properties of sci-
entific publications, References and Pages are included in
the models. Our theory offers a simpler explanation why the
more references a paper cites, the more citations it appears
to get. Due to the boundary-spanning synthetic mechanism,
an article needs to explain multiple parts and how they can
be innovatively connected. This process will result in citing
more references than would an article that covers a narrower
range of topics. Review papers by their nature belong to this
It is known that articles published earlier tend to have
more citations than do articles published later. The exposure
time of an article is included in the NB models in terms of a
logarithmically transformed year of publication of an article.
An intuitive way to interpret coefficients in NB models is
to use incidence rate ratios (IRRs) estimated by the models.
For example, if Coauthors has an IRR of 1.5, it means that
as the number of coauthors increases by 1, the global citation
counts would be expected to increase by a factor of 1.5 (i.e.,
increasing 1.5 times) while holding other variables in the
model constant. In our models, we will particularly examine
statistically significant IRRs of structural variation models.
ZINB models use the same set of variables. The count
model of ZINB is identical to the NB model described ear-
lier. The zero-inflated model of the ZINB uses the same set
of variables to predict the excessive zeros. We found little
in the literature about good predictors of zeros in a com-
parable context. We choose to include all six variables in
the zero-inflated model to provide a broader view of the
zero-generating process. ZINBs are defined as follows:
Global citations Coauthors +Modularity Change Rate
+Cluster Linkage +Centrality Divergence
+References +Pages.
Zero citations Coauthors +Modularity Change Rate
+Cluster Linkage +Centrality Divergence
+References +Pages.
Unless stated otherwise, baseline networks are formed
with a 2-year sliding window [Y 2, Y 1] for papers pub-
lished in year Y. Co-citation links made by the top-100 most
cited articles in each year of the sliding window are used to
construct a baseline network.
Complex Network Analysis (1996–2004)
Figure 4 shows an overview of a network of co-cited ref-
erences in this field. Only major clusters’ labels are shown
in the figure. The largest cluster (Cluster 8) is labeled com-
plex network. The groups of clusters in the lower left of
the visualization include social capital (Cluster 12) and old-
helping-partner (Cluster 11). The groups of clusters in the
lower right of the visualization include striate cortical activ-
ity (Cluster 2), stochastic resonance (Cluster 1), and phase
DOI: 10.1002/asi
FIG. 4. A network of co-cited references derived from the Complex NetworkAnalysis (1996–2004). Dashed lines in red are novel connections made by the
groundbreaking article (Watts & Strogatz, 1998), which has the highest scores in both Cluster Linkage (5.43) and Centrality Divergence (1.14). [Color figure
can be viewed in the online version, which is available at]
synchronization (Cluster 5). Dashed lines in red are novel con-
nections made by Watts and Strogatz (1998) at the time of its
publication. The article has the highest scores in CL and CKL
scores; 5.43 and 1.14, respectively. The figure offers a visual
confirmation that the article was indeed making boundary-
spanning connections. Recall that the dataset was constructed
by expanding the seed article based on forward citation links.
These boundary-spanning links provide empirical evidence
that the groundbreaking paper was connecting two groups of
clusters. The emergence of Cluster 8 complex network was
the consequence of the impact.
Figure 5 is a close-up view of the lower right region shown
in Figure 4. Dashed lines in red depict the novel links made
by Watts and Strogatz in 1998. The references connected by
the new links are labeled.
Table 1 summarizes the results of five NB regression mod-
els with different types of networks. They have an average
dispersion parameter θof 0.5270, which is equivalent to an α
of 1.8975. Coauthors has an average IRR of 1.3278, Ref-
erences has an average IRR of 1.0126, and Pages has an
average IRR of 0.9714. The effects of the three variables are
consistent and stable across the five types of networks. In con-
trast, the effects of structural variations are less stable. On the
other hand, structural variations appear to have a stronger
impact on global citations than do other more commonly
studied measures such as Coauthors and References.For
example, CL has an IRR of 3.160 in networks of co-cited refer-
ences and an IRR of 1.33 ×108in networks of noun phrases.
IRRs that are greater than 1.0 predict an increase of global
In theory, ZINB regression models may more accurately
estimate the effects of variables, especially in situations where
an excessive number of zeros is expected. It is widely known
that a considerable number of scientific publications would
never be cited. Table 2 summarizes a ZINB model derived
from networks of co-cited references (Model 1) and a corre-
sponding NB model (Model 2). The Vuong test indicated that
the ZINB model is superior to the NB model at p=0.0033.
The IRRs of References and Pages remain identical in
both models. The IRRs of Coauthors, MCR, and CL are
slightly lower in the ZINB model than those in the NB
model. Two variables have statistically significant effects in
the zero-inflation model: MCR and References. In summary,
for this particular dataset, CL is a more prominent predictor
of global citations than are all other more commonly stud-
ied factors. Considering the fact that CL is associated with
a clearly defined boundary-spanning mechanism, this find-
ing provides empirical evidence that such mechanisms are
likely to reveal some fundamental insights into the creation
of scientific knowledge.
Mass Extinction (1991–2010)
The Vuong test was not able to reject the hypothesis that
the ZINB model and the NB model are indistinguishable at
p=0.0928. In particular, none of the variables have statisti-
cally significant effects on predicting the excessive number
of zeros (Table 3).
The ZINB/NB models reveal a similar pattern as that
seen in the Complex Network Analysis dataset. Again, CL is
the most prominent predictor of global citation counts, with
the strongest IRR coefficient of 3.975 in the ZINB model.
DOI: 10.1002/asi
FIG. 5. A close-up view of the lower right region shown in Figure 4, showing novel links between distinct clusters of co-cited references. [Color figure can
be viewed in the online version, which is available at]
TABLE 1. Negative binomial regression (NB) models of complex network analysis (1996–2004) at five different levels of granularity of units of
Data source: Complex network analysis (1996–2004), top-100 records per time slice, 2-year sliding window
Unit of analysis Reference Keyword Noun phrase Author Journal
Relation Co-citation Co-occurrence Co-occurrence Co-citation Co-citation
Offset (exposure) log2(Year) log2(Year) log2(Year) log2(Year) log2(Year)
No. of citing articles 3,515 3,072 3,254 3,271 3,271
Global citations Incidence rate ratios in NB models
Coauthors 1.306 0.000 1.298 0.000 1.326 0.000 1.359 0.000 1.350 0.000
Modularity change rate 1.083 0.025 1.038 0.086 1.047 0.305 1.055 0.276 1.060 0.180
Weighted cluster linkage 3.160 0.000 0.205 0.095 1.33 ×1080.000 2.879 0.000 1.204 0.049
Centrality Divergence 0.343 0.184 3.679 0.023 1.534 0.665 23.400 0.000 7.620 0.000
No. of references 1.013 0.000 1.013 0.000 1.013 0.000 1.012 0.000 1.012 0.000
No. of pages 0.970 0.000 0.971 0.000 0.971 0.000 0.973 0.000 0.972 0.000
Dispersion parameter (θ) 0.5284 0.5258 0.5150 0.5282 0.5375
2×log-likelihood 31,771 28,331 29,491 29,506 29,613
AIC 31,787 28,347 29,508 29,522 29,629
AIC =Akaike’s Information Criterion.
aReferences involve the least amount of ambiguity with the finest granularity whereas the other four types of units introduce ambiguity at various levels.
Models constructed with units of higher ambiguity are slightly improved in terms of AIC.
DOI: 10.1002/asi
TABLE 2. Zero-inflated negative binomial regression (ZINB) and negative binomial regression (NB) models of global citation counts of 3,515 citing articles
on complex network analysis (1996–2004) with reference to networks of co-cited references using log2(year of publication) to account for the exposure
Global cites Count model Negbin with log link Zero-inflation model Binomial with logit link NB
Coauthors 1.293 0.000 0.062 0.077 1.306 0.000
Modularity change rate 1.080 0.014 0.012 0.044 1.083 0.025
Weighted cluster linkage 3.103 0.000 1.304 0.906 3.160 0.000
Centrality Divergence 0.391 0.237 385363.6 0.102 0.343 0.184
No. of references 1.013 0.000 0.489 0.027 1.013 0.000
No. of pages 0.970 0.000 1.133 0.120 0.970 0.000
Dispersion parameter (θ) 0.536 0.528
AIC 31,768 31,787
Vuong test (ZINB>NB) 2.7186, p=0.0033
AIC =Akaike’s Information Criterion.
aCoefficients are incidence rate ratios. Weighted cluster linkage is the strongest predictor of citation counts, followed by the number of coauthors, and the
modularity change rate. In this case, with a lower AIC and a statistically significant Vuong test, the ZINB model is superior.
TABLE 3. Zero-inflated negative binomial regression (ZINB) and negative binomial regression (NB) models of global citation counts of 1,745 articles on
Mass Extinctions (1991–2010) with reference to networks of co-cited references using log2(year of publication) to account for the exposure time.a
Global cites Count model Negbin with log link Zero-inflation model Binomial with logit link NB
Coauthors 1.160 0.001 0.191 0.380 1.162 0.0006
Modularity change rate 0.713 0.021 9.95 ×1090.269 0.736 0.0374
Weighted cluster linkage 3.975 0.000 2.63 ×1050.461 3.805 0.0000
Centrality Divergence 9.837 0.334 3.65 ×1020 0.877 8.619 0.3607
No. of references 1.004 0.000 1.025 0.283 1.004 0.0000
No. of pages 0.987 0.002 0.657 0.116 0.988 0.0048
Dispersion parameter (θ) 0.489 0.484
AIC 13,368 13,366
Vuong test (ZINB>NB) 1.3234, p=0.0928
AIC =Akaike’s Information Criterion.
aCoefficients are incidence rate ratios. Cluster linkage is the strongest predictor of citation counts, followed by the number of coauthors and the number
of references, respectively. In this case, the ZINB and NB models are indistinguishable statistically.
The IRRs of Coauthors and References are slightly greater
than 1.0. The IRRs of MCR and Pages are less than 1.0, indi-
cating an expected decrease of citations as the two variables
increase independently.
Terrorism (1996–2005)
Unlike the previous cases in which 1-year time slices
were used, we used time slices with longer exposure time
for the Terrorism (1996–2005) dataset, including 2- and 3-
year time slices. A 3-year moving window was used with
3-year time slices.
The ZINB and NB models revealed similar patterns to other
cases we have seen so far, except the strong IRR of Centrality
Divergence, which was absent in the earlier cases. The effects
of Coauthors,CL,References, and Pages are within the sim-
ilar range as shown in earlier cases. Table 4 includes models
for both 2- and 3-year time slices. The 3-year models have a
better relative goodness of fit than the 2-year models accord-
ing to the AIC scores. The IRR of Centrality Divergence is
48.061 in 3-year slice models and 62.637 in 2-year slice mod-
els. We inspected articles with high Centrality Divergence to
find an explanation of the strong Centrality Divergence.
Figure 6 illustrates connections made by the top-five arti-
cles with the strongest Centrality Divergence scores based
on 2-year time slice models. As show in the figure, much of
the boundary-spanning activities introduced by this group of
articles involve three major clusters: Cluster 12 biological ter-
rorism, Cluster 13 ocular injury, and Cluster 5 September 11.
The five top-ranked high-Centrality Divergence articles are
listed in Table 5, including their DOIs. One can tell from
their titles which topic areas that they were connecting; for
example, connections between terrorism and medical services
(Papers 1 and 3) and connections between PTSD and Septem-
ber 11 terrorist attacks (Papers 2, 4, and 5). In terms of the
clusters shown in Figure 6, the topic of bioterrorism is clearly
associated with Cluster 12, the topic of September 11 terrorist
attacks is the focus of Cluster 5, and the topic of clinical and
medical services is closely related to Cluster 13.
The current analysis suggests that the strong IRR of
Centrality Divergence may be due to the wide-ranging inter-
disciplinary structure of the subject matter. If this is indeed
the case, then a strong Centrality Divergence can be a valu-
able early sign of transformative research at interdisciplinary
DOI: 10.1002/asi
TABLE 4. Zero-inflated negative binomial regression (ZINB) and negative binomial regression (NB) models of global citation counts of 3,476 articles on
Terrorism (1996–2005) with reference to networks of co-cited references using log2(year of publication) to account for the exposure time.a
Global cites Count model Negbin with log link Zero-inflation model Binomial with logit link NB
Slice length =3 years
Coauthors 1.808 0.0000 0.000 0.9524 1.960 0.0000
Modularity change rate 0.578 0.0973 1.563 0.8713 0.581 0.0603
Weighted cluster linkage 3.306 0.0001 0.000 0.7251 3.300 0.0002
Centrality Divergence 48.061 0.0012 0.121 0.9368 64.120 0.0000
No. of references 1.018 0.0000 0.980 0.0702 1.019 0.0000
No. of pages 0.985 0.0000 1.002 0.8916 0.986 0.0048
Dispersion parameter (θ) 0.520 0.464
AIC 20,591 20,619
Vuong test (ZINB>NB) 2.9912, p=0.0014
Slice length =2 years
Coauthors 1.853 0.0000 0.067 0.0119 2.005 0.0000
Modularity change rate 0.586 0.0000 0.394 0.3708 0.596 0.0000
Weighted cluster linkage 3.180 0.0000 28.363 0.0435 2.674 0.0000
Centrality Divergence 62.637 0.0319 0.000 0.2143 108.959 0.0046
No. of references 1.018 0.0000 0.984 0.1296 1.019 0.0000
No. of pages 0.986 0.0000 1.004 0.6902 0.986 0.0000
Dispersion parameter (θ) 0.518 0.464
AIC 20,668 20,695
Vuong test (ZINB>NB) 2.8463, p=0.0022
AIC =Akaike’s Information Criterion.
aThe length of each time slice and the width of the moving window are both 3 years. Coefficients are IRRs.The strongest predictor is Centrality Divergence,
followed by weighted cluster linkage, the number of coauthors, and the number of references. With a lower AIC and a statistically significant Vuong test, the
ZINB model is a better model than is the NB model.
FIG. 6. Co-citations made by the five articles with the strongest Centrality Divergence scores in Terrorism (1996–2005) that reinforced existing patterns
(left) and introduced novel connections (right). Structural variations were computed based on 2-year time slices. [Color figure can be viewed in the online
version, which is available at]
levels. Further research is needed to gain additional insights
into implications of these new structural variation metrics.
CiteSpace Expanded (2001–2010)
This dataset was formed with a seed article (Chen, 2006)
by including papers that have at least one cited reference in
common with the seed article during the period of 2001 and
2010. Estimates made by ZINB and NB models are listed in
Table 6.TheVuong test was not able to reject the null hypoth-
esis that the ZINB and NB models are indistinguishable.
A ZINB regression model shows a statistically significant
DOI: 10.1002/asi
TABLE 5. The top-five articles in Terrorism (1996–2005) with the strongest Centrality Divergence scores. Co-citation connections made by these papers
are shown in Figure 6.
No. Cites CKL Author Year Title. Source. DOI
1 23 0.500 Flowers, L.K. 2002 Bioterrorism preparedness II: The community and emergency medical services systems. Emergency
Medicine Clinics of North America.10.1016/S0733-8627(01)00009-8
2 35 0.327 Grieger, T.A. 2003 Posttraumatic stress disorder alcohol use and perceived safety after the terrorist attack on the Pentagon.
Psychiatric Services. 10.1176/
3 72 0.285 Stein, M. 1999 Medical consequences of terrorism—The conventional weapon threat. Surgical Clinics of North America.
4 80 0.271 Ahern, J. 2002 Television images and psychological symptoms after the September 11 terrorist attacks. Psychiatry.
5 23 0.264 Ford, C.A. 2003 Reactions of young adults to September 11 2001. Archives of Pediatrics & Adolescent Medicine.
TABLE 6. Zero-inflated negative binomial regression (ZINB) and negative binomial regression (NB) models of global citation counts of 3,260 articles
published between 2001 and 2010 in an area seeded by our 2006 Journal of the American Society of Information Science and Technology article with reference
to networks of co-cited references using log2(year of publication) to offset the exposure time.a
Global cites Count model Negbin with log link Zero-inflation model Binomial with logit link NB
Coauthors 1.070 0.0826 4.125 0.3379 1.084 0.0225
Modularity change rate 1.025 0.5482 1.715 0.0198 1.013 0.7135
Weighted cluster linkage 3.230 0.0046 0.001 0.0834 3.589 0.0000
Centrality Divergence 3.203 0.0870 0.000 0.1590 3.453 0.0272
No. of references 1.010 0.0000 0.958 0.0124 1.010 0.0000
No. of pages 0.985 0.0000 1.097 0.0844 0.984 0.0048
Dispersion parameter (θ) 0.423 0.413
AIC 19,797 19,786
Vuong test (ZINB>NB) 0.4694, p=0.3194
AIC =Akaike’s Information Criterion.
aWeighted cluster linkage is the strongest predictor of citation counts, followed by the number of references. In this case, the ZINB and NB models are
not indistinguishable statistically.
TABLE 7. The Akaike’s Information Criterion (AIC) of zero-inflated negative binomial regression (ZINB) and negative binomial regression (NB) models.
ZINB models are distinguishable from NB models in three of the five cases. AICs increase as the number of papers per time slice increases.
Cases Seeded From Years nn/Year AIC (ZINB) AIC (NB) ZINB >NB (p)
Mass Extinctions No 1991–2010 20 1,745 87.25 13,368 13,366 0.0928
CiteSpace Expanded Yes 2001–2010 10 3,260 163.00 19,797 19,786 0.3194
Terrorism (3-year slices) No 1996–2005 10 3,476 173.80 20,591 20,619 0.0014
Terrorism (2-year slices) No 1996–2005 10 3,476 173.80 20,668 20,695 0.0022
Complex Networks Yes 1996–2004 9 3,515 175.75 31,768 31,787 0.0033
main effect of the CL variation with an IRR of 3.230. Ref-
erences and Pages have similar values as those in other cases.
The NB model reveals a strong effectof Centrality Divergence
(IRR =3.453).
The seed article describes CiteSpace, a visual analytic tool
for identifying emerging trends and changes in scientific lit-
erature. It has been cited 100 times in the Web of Science and
has the highest CL score (12.78) and the highest Centrality
Divergence score (1.24).
ZINB models are superior to their NB counterparts in the
three cases we studied, but indistinguishable in the other two
cases (see Table 7). The AIC values of these models increase
as the number of citing articles per time slices increases,
but there is no apparent trend in connection to whether
the datasets are seeded. Mass Extinctions and CiteSpace
Expanded have the lowestAIC values whereas Complex Net-
works has the highest AIC value. In the Terrorism case, the
use of 3-year time slices improved the models over the 2-
year configurations. These results seem to suggest that ZINB
models are particularly recommended as the size of a dataset
increases. As the number of articles increases, more and more
zero-citation articles need to be accounted for.
The IRR of a variable has an intuitive interpretation. If it
is greater than 1.0 and all other variables are held constant,
DOI: 10.1002/asi
TABLE 8. Incidence rate ratios (IRRs) of predictors of global citation counts.
Cases Coauthors MCR Cluster linkage Centrality Divergence References Length in pages
Mass Extinctions 1.160a0.713 3.975 9.837 1.004 0.987
CiteSpace Expanded 1.084 1.013 3.589 3.453 1.010 0.984
Terrorism (3-year slices) 1.808 0.578 3.306 48.061 1.018 0.985
Terrorism (2-year slices) 1.853 0.586 3.180 62.637 1.018 0.986
Complex Networks 1.293 1.080 3.103 0.391 1.013 0.970
M1.440 0.794 3.431 24.876 1.013 0.982
MCR =modularity change rate.
aThe IRRs in bold are statistically significant at p=0.05. The IRRs of coauthors, cluster linkage, references, and pages are consistent across the cases
whereas the IRRs of MCR and Centrality Divergence are mixed.
then the citation count is expected to increase along with a
unit increase of the variable. In contrast, if it is less than 1.0,
then the citation count is expected to decrease as the variable
increases. Cluster Linkage,Coauthors, and References are
persistently found to predict an increase of citation counts.
The strongest predictor, Cluster Linkage, has an average IRR
of 3.431, which is more than twice that of the second strongest
predictor, Coauthors (average IRR =1.440) and more than
three times that of the third strongest predictor, References
(average IRR =1.013).The length of an article in terms of the
number of pages has a minor, but consistent, negative impact
with an average IRR of 0.982 across all five cases.
In contrast, the IRR estimates of Centrality Diver-
gence and MCR did not give clear signals. Although the
largest average IRR (24.876) is found with Centrality Diver-
gence and although in three of the five cases the effect was
positive and statistically significant, it has a negative impact
on citations in the Complex Network case. It is possible
that Centrality Divergence is sensitive to the structure of
the underlying network as well as to the structural change
of the network. Given its strong IRRs in its magnitude and
the possible sensitivity, the behavior of the metric should
be investigated further to explain the discrepancies across
different cases.
MCR is an indirect measure of the impact of cross-cluster
links. It has an average IRR of 0.794, but the estimated IRRs
are inconsistent across the five cases in terms of statistical
significance and the direction of citation change. The dis-
crepancies may reflect the connectivity of individual nodes
involved. For example, adding a new link to a node with either
a large degree or a small degree is likely to affect the MCR
much more than is linking two nodes of similar connectivity.
Cluster Linkage is a more direct measure of intellectual
potential than are Coauthors and References.Cluster Linkage
has a simple and clear theoretical interpretation supported by
the underlying boundary-spanning mechanism. The strong
IRRs of Cluster Linkage suggest that the structural variation
metric is more efficient for predicting the growth of citation
counts than are indicators such as Coauthors and References.
These findings are very encouraging because of their
theoretical and practical implications. These findings are
valuable in improving our understanding of how transfor-
mative ideas can be made and recognized. The premise of
the structural variation approach is that transformative ideas
are expected to introduce significant and computationally
detectable structural changes. The value of such approaches
has been identified by many researchers (Boyack et al., 2005;
Leydesdorff, 2001; Shibata et al., 2007; Upham et al., 2010;
van Dalen & Kenkens, 2005). The boundary-spanning mech-
anism provides one possible explanation of what factors
attract citations. It is conceivable that an idea has the potential
to introduce novel structural changes, but fails to material-
ize it due to many reasons. In the present study, we use the
co-citation network immediately prior to the publication of
a new paper as the reference point. There are several other
options at various levels of granularity and scale. For instance,
in the Complex Network Analysis case, structural variation
models based on networks of co-occurring keywords have the
best AIC score with an IRR of Centrality Divergence of 3.679.
Models based on networks of co-cited authors led to a strong
Centrality Divergence IRR of 23.400. We did not investigate
the implications of these results in the present article, but we
believe that they do indicate a wide range of potentially sig-
nificant directions of research that is far beyond the scope of
a single study.
We have found statistical evidence of the boundary-
spanning mechanism. An article that introduces novel con-
nections between clusters of co-cited references is likely to
subsequently become highly cited. In addition, we have found
that the IRRs of Cluster Linkage are more than twice as much
as the IRRs of Coauthors and References. This finding pro-
vides a more fundamental explanation of why the number of
references cited by an article appears to be a good predictor
of its future citations, as found in many previous studies.As a
result, the structural variation paradigm clarifies why a num-
ber of extrinsic features appear to be associated with high
A distinct characteristic of the structural variation
approach is the focus on the potential connection between
the degree of structural variation introduced by an article
and its future impact. The analytic and modeling procedure
demonstrated in this article is expected to serve as an exem-
plar for subsequent studies along this line of research. More
DOI: 10.1002/asi
important, the focus on the underlying mechanisms of sci-
entific activity is expected to provide additional insights and
practical guidance for scientists, sociologists, historians, and
philosophers of scientific knowledge.
There are many new challenges and opportunities ahead.
For example, how common is the boundary-spanning mech-
anism in scientific discoveries overall? What are the other
major mechanisms, and how do they interact with the
boundary-spanning mechanism? There are other potentially
valuable techniques that we have not utilized in the present
study, including topic modeling, citation context analysis,
survival analysis, and burst detection. In short, a lot of work
needs to be done, and this is an encouraging start.
We conclude that structural variation is an essential aspect
of the development of scientific knowledge and it has the
potential to reveal the underlying mechanisms of the growth
of scientific knowledge. The focus on the underlying mecha-
nisms of knowledge creation is the key to the predictive poten-
tial of the structural variation approach. The theory-driven
explanatory and computational approach sets an extensible
framework for detecting and tracking potentially creative
ideas and gaining insights into challenges and opportunities
in light of the collective wisdom.
I thank the anonymous reviewers for their valuable
Aksnes, D.W. (2003). Characteristics of highly cited papers. Research
Evaluation, 12(3), 159–170.
Barabási, A.L., & Albert, R. (1999). Emergence of scaling in random
networks. Science, 286(5439), 509–512.
Bornmann, L., & Daniel, H.-D. (2006). What do citation counts measure?
A review of studies on citing behavior. Journal of Documentation, 64(1),
Boyack, K.W., Klavans, R., Ingwersen, P., & Larsen, B. (2005, July). Pre-
dicting the importance of current papers. Paper presented at the 10th
International Conference of the International Society for Scientometrics
and Informetrics, Stockholm, Sweden. Retrieved from https://cfwebprod
Brody, T., & Harnad, S. (2005). Earlier web usage statistics as predictors of
later citation impact. Retrieved from
Burt, R.S. (2004). Structural holes and good ideas. American Journal of
Sociology, 110(2), 349–399.
Buter, R., Noyons, E., & van Raan, A. (2011). Searching for converging
research using field to field citations. Scientometrics, 86(2), 325–338.
Chen, C. (2003). Mapping scientific frontiers: The quest for knowledge
visualization. London: Springer-Verlag.
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and
transient patterns in scientific literature. Journal of the American Society
for Information Science and Technology, 57(3), 359–377.
Chen, C. (2011). Turning points: The nature of creativity. New York:
Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009).
Towards an explanatory and computational theory of scientific discovery.
Journal of Informetrics, 3(3), 191–209.
Chen, C., Cribbin, T., Macredie, R., & Morar, S. (2002). Visualizing and
tracking the growth of competing paradigms: Two case studies. Journal
of the American Society for Information Science and Technology, 53(8),
Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010).The structure and dynamics
of co-citation clusters:A multiple-perspective co-citation analysis. Journal
of the American Society for Information Science and Technology, 61(7),
Chen, C., Lin, X., & Zhu, W. (2006, November). Trailblazing through a
knowledge space of science: Forwardcitation expansion in CiteSeer. Paper
presented at the 69th annual meeting of the American Society for Infor-
mation Science and Technology (ASIS&T 2006), Austin, TX. Retrieved
Chubin, D.E. (1994 ). Grants peer-review in theory and practice. Evaluation
Review, 18(1), 20–30.
Chubin, D.E., & Hackett, E.J. (1990). Paperless science: Peer review and
U.S. science policy. Albany, NY: State University of New York Press.
Cuhls, K. (2001). Foresight with Delphi surveys in Japan. Technology
Analysis & Strategic Management, 13(4), 555–569.
Dewett, T., & Denisi, A.S. (2004). Exploring scholarly reputation: It’s more
than just productivity. Scientometrics, 60(2), 249–272.
Fauconnier, G., & Turner, M. (1998). Conceptual integration networks.
Cognitive Science, 22(2), 133–187.
Fleming, L., & Bromiley, P. (2000, December). A variable risk propen-
sity model of technological risk taking. Paper presented at the Applied
Statistics Workshop. Retrieved from
Galea, S., Ahern, J., Resnick, H., Kilpatrick, D., Bucuvalas, M., Gold, J., &
Vlahov, D. (2002). Psychological sequelae of the September 11 terrorist
attacks in New York City. New England Journal of Medicine, 346(13),
Garfield, E. (1955). Citation indexes for science: A new dimension in
documentation through association of ideas. Science, 122, 108–111.
Haslam, N., Ban, L., Kaufmann, L., Loughnan, S., Peters, K., Whelan, J., &
Wilson, S. (2008). What makes an article influential? Predicting impact
in social and personality psychology. Scientometrics, 76(1), 169–185.
Häyrynen, M. (2007). Breakthrough research: Funding for high-risk research
at the Academy of Finland. Helsinki: Academy of Finland.
Hettich, S., & Pazzani, M.J. (2006). Mining for proposal reviewers: Lessons
learned at the National Science Foundation. In Proceedings of the 12th
ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining (pp. 862–871). New York: ACM Press.
Hilbe, J.M. (2011). Negative binomial regression (2nd ed.). New York:
Cambridge University Press.
Hirsch, J.E. (2007). Does the h index have predictive power? Proceedings
of the National Academy of Sciences, USA, 104(49), 19193–19198.
Hsieh, C. (2011). Explicitly searching for useful inventions: Dynamic relat-
edness and the costs of connecting versus synthesizing. Scientometrics,
86(2), 381–404.
Kostoff, R. (2007). The difference between highly and poorly cited medical
articles in the journal Lancet. Scientometrics, 72, 513–520.
Kurtz, M.J., Eichhorn, G., Accomazzi, A., Grant, C., Demleitner, M.,
Henneken, E., & Murray, S.S. (2005). The effect of use and access on
citations. Information Processing & Management, 41(6), 1395–1402.
Lahiri, M., Maiya, A.S., Sulo, R., Habiba, & Wolf, T.Y.B. (2008,
December). The impact of structural changes on predictions of diffusion
in networks. Paper presented at the the 2008 IEEE International Con-
ference on Data Mining Workshops (ICDMW ’08), Pisa, Italy. Retrieved
Lambert, D. (1992). Zero-infated Poisson regression, with an application to
defects in manufacturing. Technometrics, 34, 1–14.
Levitt, J., & Thelwall, M. (2008). Patterns of annual citation of highly cited
articles and the prediction of their citation ranking: A comparison across
subjects. Scientometrics, 77(1), 41–60.
Leydesdorff, L. (2001). The challenge of scientometrics: The development,
measurement, and self-organization of scientific communications. Boca
Raton, FL: Universal Publishers.
Lipinski, C., & Hopkins, A. (2004). Navigating chemical space for biology
and medicine. Nature, 432(7019), 855–861.
DOI: 10.1002/asi
Luxburg, U. von. (2006). A tutorial on spectral clustering. Retrieved
Martin, B.R. (2010). The origins of the concept of “foresight” in science
and technology: An insider’s perspective. Technological Forecasting and
Social Change, 77(9), 1438–1447.
Merton, R.K. (1968). The Mathew Effect in science. Science, 159(3810),
Miles, I. (2010). The development of technology foresight: A review.
Technological Forecasting and Social Change, 77(9), 1448–1456.
Newman, M.E.J. (2006). Modularity and community structure in net-
works. Proceedings of the National Academy of Sciences, USA, 103(23),
Persson, O. (2010). Are highly cited papers more international? Scientomet-
rics, 83(2), 397–401.
Price, D.D. (1965). Networks of scientific papers. Science, 149, 510–515.
Shibata, N., Kajikawa, Y., & Matsushima, K. (2007). Topological analy-
sis of citation networks to discover the future core articles. Journal of
the American Society for Information Science and Technology, 58(6),
Skilton, P. (2009). Does the human capital of teams of natural science authors
predict citation frequency? Scientometrics, 78(3), 525–542.
Swanson, D.R. (1986a). Fish oil, Raynaud’s syndrome, and undiscovered
public knowledge. Perspectives in Biology and Medicine, 30, 7–18.
Swanson, D.R. (1986b). Undiscovered public knowledge. Library Quarterly,
56(2), 103–118.
Takeda,Y., & Kajikawa,Y. (2010). Tracking modularity in citation networks.
Scientometrics, 83(3), 783.
Tichy, G. (2004). The over-optimism among experts in assessment
and foresight. Technological Forecasting and Social Change, 71(4),
Tijssen, R.J.W., Visser, M.S., & van Leeuwen, T.N. (2002). Benchmarking
international scientific excellence: Are highly cited research papers an
appropriate frame of reference? Scientometrics, 54(3), 381–397.
Upham, S.P., Rosenkopf, L., & Ungar, L.H. (2010). Positioning knowl-
edge: Schools of thought and new knowledge creation. Scientometrics, 83,
van Dalen, H.P., & Kenkens, K. (2005). Signals in science: On the impor-
tance of signaling in gaining attention in science. Scientometrics, 64(2),
Walters, G.D. (2006). Predicting subsequent citations to articles published in
twelve crime-psychology journals: Author impact versus journal impact.
Scientometrics, 69(3), 499–510.
Watts, D.J., & Strogatz, S.H. (1998). Collective dynamics of “small-world”
networks. Nature, 393(6684), 440–442.
Weeber, M. (2003). Advances in literature-based discovery. Journal of
the American Society for Information Science and Technology, 54(10),
Zeileis, A., Kleiber, C., & Jackman, S. (2011). Regression models for count
data in R. Retrieved from
... Using the structural variant analysis and burst term analysis functions of CiteSpace, we examined important items influencing the structure of mapping networks in the present study, making it possible to assess potential future research directions. In the structural variation analysis, the degree of structural variation introduced by a new article can provide prospective information based on the boundary spanning mechanism [32]. If an article introduces new linkages across different subject boundaries, we expect that it has the potential to bring the knowledge structure to a new turning point, which can be an important bridge and focus for future research [32]. ...
... In the structural variation analysis, the degree of structural variation introduced by a new article can provide prospective information based on the boundary spanning mechanism [32]. If an article introduces new linkages across different subject boundaries, we expect that it has the potential to bring the knowledge structure to a new turning point, which can be an important bridge and focus for future research [32]. Another key method, burst term detection, is capable of identifying meaningful and bursting structures in the document stream over time based on data streaming algorithms [33]. ...
... The silhouette score (S) measured the homogeneity within clusters, ranging from − 1 to 1, and S > 0.7 indicated a high level of resemblance among nodes within each cluster. Furthermore, we employed centrality divergence, a measure of the dispersion of betweenness centrality distributions of nodes, to assess the innovation of citing documents in the structural variation network [32]. ...
Full-text available
Background Multimorbidity and frailty represent emerging global health burdens that have garnered increased attention from researchers over the past two decades. We conducted a scientometric analysis of the scientific literature on the coexistence of multimorbidity and frailty to assess major research domains, trends, and inform future lines of research. Methods We systematically retrieved scientific publications on multimorbidity and frailty from the Web of Science Core Collection, spanning from 2003 to 2023. Scientometric analysis was performed using CiteSpace and VOSviewer, enabling the visualization and evaluation of networks comprising co-citation references, co-occurring keywords, countries, institutions, authors, and journals. Results A total of 584 eligible publications were included in the analysis. An exponential rise in research interest in multimorbidity and frailty was observed, with an average annual growth rate of 47.92% in publications between 2003 and 2022. Three major research trends were identified: standardized definition and measurement of multimorbidity and frailty, comprehensive geriatric assessment utilizing multimorbidity and frailty instruments for older adults, and the multifaceted associations between these two conditions. The United States of America, Johns Hopkins University, Fried LP, and the Journal of the American Geriatrics Society were identified as the most influential entities within this field, representing the leading country, institution, author, and journal, respectively. Conclusions Scientometric analysis provides invaluable insights to clinicians and researchers involved in multimorbidity and frailty research by identifying intellectual bases and research trends. While the instruments and assessments of multimorbidity and frailty with scientific validity and reliability are of undeniable importance, further investigations are also warranted to unravel the underlying biological mechanisms of interactions between multimorbidity and frailty, explore the mental health aspects among older individuals with multimorbidity and frailty, and refine strategies to reduce prescriptions in this specific population.
... In Figure 4, the authors and publication years of the top 10 nodes with the largest size were labeled in red text, and Table 6 lists the information of these articles. The most cited articles are usually considered as landmarks due to their ground breaking contributions (Chen, 2006;Chen, 2012;Chen et al., 2012). Lee and Zhang (2015), Papenfort and Bassler (2016), and Whiteley et al. (2017) are the three papers with the highest co-citation. ...
... Structural variation analysis (SVA) can identify articles that have a significant potential to transform the network of references, as they cite and are cited by novel and diverse references, while bridging different research topics (Burt, 2004;Chen, 2012). We applied the SVA function of CiteSpace to analyze the co-citation network of the literature and obtained the ten articles with the highest structural variation potential scores based on structural properties during the period from 1998 to 2023 (Table 7). ...
Full-text available
Background Quorum sensing is bacteria’s ability to communicate and regulate their behavior based on population density. Anti-quorum sensing agents (anti-QSA) is promising strategy to treat resistant infections, as well as reduce selective pressure that leads to antibiotic resistance of clinically relevant pathogens. This study analyzes the output, hotspots, and trends of research in the field of anti-QSA against clinically relevant pathogens. Methods The literature on anti-QSA from the Web of Science Core Collection database was retrieved and analyzed. Tools such as CiteSpace and Alluvial Generator were used to visualize and interpret the data. Results From 1998 to 2023, the number of publications related to anti-QAS research increased rapidly, with a total of 1,743 articles and reviews published in 558 journals. The United States was the largest contributor and the most influential country, with an H-index of 88, higher than other countries. Williams was the most productive author, and Hoiby N was the most cited author. Frontiers in Microbiology was the most prolific and the most cited journal. Burst detection indicated that the main frontier disciplines shifted from MICROBIOLOGY, CLINICAL, MOLECULAR BIOLOGY, and other biomedicine-related fields to FOOD, MATERIALS, NATURAL PRODUCTS, and MULTIDISCIPLINARY. In the whole research history, the strongest burst keyword was cystic-fibrosis patients, and the strongest burst reference was Lee and Zhang (2015). In the latest period (burst until 2023), the strongest burst keyword was silver nanoparticle, and the strongest burst reference was Whiteley et al. (2017). The co-citation network revealed that the most important interest and research direction was anti-biofilm/anti-virulence drug development, and timeline analysis suggested that this direction is also the most active. The key concepts alluvial flow visualization revealed seven terms with the longest time span and lasting until now, namely Escherichia coli , virulence, Pseudomonas aeruginosa , virulence factor, bacterial biofilm, gene expression, quorum sensing. Comprehensive analysis shows that nanomaterials, marine natural products, and artificial intelligence (AI) may become hotspots in the future. Conclusion This bibliometric study reveals the current status and trends of anti-QSA research and may assist researchers in identifying hot topics and exploring new research directions.
... In 2006 Cite-Space II introduced Burst detection, citation tree-ring, and time zone views (Chen 2006). Since 2010, an increasing number of analyses have been refined with the gradual evolution of Cite-Space software, including cluster labelling (Chen et al. 2010), structural variation analysis (Chen 2012), cascading citation expansion (Chen and Song 2019), and citation contexts and uncertainties (Chen 2020). Currently, CiteSpace is due to more extensive features, more steady operation, and better suitability for the study (Chen 2020). ...
Full-text available
The Regional Comprehensive Economic Partnership (RCEP) is an agreement that transformed the world economy and entered into force in January 2022 with the participation of fifteen nations. In the study, the visualisation analysis was 301 articles in Web of Science (WoS) on the subjects of “RCEP,” or “The Regional Comprehensive Economic Partnership,” from January 2012 to January 2023, using CiteSpace. The results of a comparative analysis of the number of journals co-citation and keyword co-occurrence indicate that further studies of “RCEP” will not be limited to the scope of traditional economics, but more and further fields are waiting for scholars to develop.
... With a clear cognition of the past information, the future trend is also predicted with CiteSpace using SVA. The principal of the analysis is to recognize the possible breakthrough of scientific papers in terms of their degrees in altering the existing intellectual structure (boundary-spanning mechanism) (Chen 2012). During these processes, the analysis was performed based on a co-citation network, similar to the work of Sebastian and Chen (2021). ...
Full-text available
Antibiotics are one of the greatest inventions in human history and are used worldwide on an enormous scale. Besides its extensive usage in medical and veterinary arenas to treat and prevent the infection, its application is very prominent in other fields, including agriculture, aquaculture, and horticulture. In recent decades, the increased consumption of antibiotics in China saw a vast increase in its production and disposal in various environments. However, in this post-antibiotic era, the abuse and misuse of these valuable compounds could lead to the unreversible consequence of drug resistance. In China, antibiotics are given a broad discussion in various fields to reveal their impact on both human/animals health and the environment. To our knowledge, we are the first paper to look back at the development trend of antibiotic-related studies in China with qualitative and quantitative bibliometric analysis from the past decades. Our study identified and analyzed 5559 papers from its inception (1991) to December 6, 2021, from the Web of Science Core Collection database. However, with few authors and institutions focusing on long-term studies, we found the quality of contributions was uneven. Studies mainly focused on areas such as food science, clinical research, and environmental studies, including molecular biology, genetics and environmental, ecotoxicology, and nutrition, which indicate possible primary future trends. Our study reports on including potentially new keywords, studies’ milestones, and their contribution to antibiotic research. We offer potential topics that may be important in upcoming years that could help guide future research.
... 45 The modularity score assesses the signicance and meaningfulness of the clustering arrangement, with values exceeding 0.3 indicating a pronounced clustering structure. 46,47 Conversely, the silhouette score measures the resemblance among data points within a cluster and the dissimilarity across various clusters; values surpassing 0.7 denote a clustering structure of high reliability. 48 In the context of this study (Fig. 3 and Table 5), the clustering structure holds signicance, and the clustering is characterized by both rationality and high reliability. ...
Full-text available
Lignocellulosic biomass (LCB) stands as a substantial and sustainable resource capable of addressing energy and environmental challenges. This study employs bibliometric analysis to investigate research trends in lactic acid (LA) production from LCB spanning the years 1991 to 2022. The analysis reveals a consistent growth trajectory with minor fluctuations in LA production from LCB. Notably, there's a significant upswing in publications since 2009. Bioresource Technology and Applied Microbiology and Biotechnology emerge as the top two journals with extensive contributions in the realm of LA production from LCB. China takes a prominent position in this research domain, boasting the highest total publication count (736), betweenness centrality value (0.30), and the number of collaborating countries (42), surpassing the USA and Japan by a considerable margin. The author keywords analysis provides valuable insights into the core themes in LA production from LCB. Furthermore, co-citation reference analysis delineates four principal domains related to LA production from LCB, with three associated with microbial conversion and one focused on chemical catalytic conversion. Additionally, this study examines commonly used LCB, microbial LA producers, and compares microbial fermentation to chemical catalytic conversion for LCB-based LA production, providing comprehensive insights into the current state of this field and suggesting future research directions.
... basic situation of a certain knowledge field, clarify its knowledge structure, detect its development trend and present it in a visual way" (Chen, 2012). ...
Full-text available
Based on the data of research papers published between 1988 to 2023, which were retrieved from China National Knowledge Infrastructure (CNKI), this paper makes a scientometric analysis of the research focuses of English translations studies of Tao Te Ching in China by employing CiteSpace. The findings were presented in knowledge domains. Aiming to uncover the research development and hot-discussed topics, the paper probes into the number of published articles, the high-impact authors and institutions, high-frequency keywords. It is found that translator research and translation strategies are the focuses of this research field. Current research mainly focuses on three aspects: translators such as Roger T. Ames, Lin Yutang, D. C. Lau, and Legge; translation strategies such as domestication and foreignization; English translation of key words such as Tao.
Full-text available
Purpose Low back pain (LBP) is a prevalent musculoskeletal disorder, and manual therapy (MT) is frequently employed as a non-pharmacological treatment for LBP. This study aims to explore the research hotspots and trends in MT for LBP. MT has gained widespread acceptance in clinical practice due to its proven safety and effectiveness. The study aims to analyze the developments in the field of MT for LBP over the past 23 years, including leading countries, institutions, authoritative authors, journals, keywords, and references. It endeavors to provide a comprehensive summary of the existing research foundation and to analyze the current cutting-edge research trends. Methods Relevant articles between 2000 and 2023 were retrieved from the Web of Science Core Collection (WOSCC) database. We used the software VOSviewer and CiteSpace to perform the analysis and summarize current research hotspots and emerging trends. Results Through screening, we included 1643 papers from 2000 to 2023. In general, the number of articles published each year showed an upward trend. The United States had the highest number of publications and citations. Canadian Memorial Chiropractic College was the most published research institution. The University of Pittsburgh in the United States had the most collaboration with other research institutions. Long, Cynthia R. was the active author. Journal of Manipulative and Physiological Therapeutics was the most prolific journal with 234 publications. Conclusion This study provides an overview of the current status and trends of clinical studies on MT for LBP in the past 23 years using the visualization software, which may help researchers identify potential collaborators and collaborating institutions, hot topics, and new perspectives in research frontiers, while providing new clinical practice ideas for the treatment of LBP.
Full-text available
Introduction Dysplastic nevi are pigmented lesions that exhibit clinical and histological features of both common nevi and melanoma. In recent years, there has been an increase in publications on dysplastic nevi. Bibliometric analysis is a method of evaluating trends in large number of publications and identifying popular topics. Objectives The objective of this study is to provide an overview of the landscape of publications related to dysplastic nevi, visualize trends and identify popular topics in the literature. Methods Thomson Reuters’ Web of Science database was searched with the following query in title, abstract or keywords: TS = (“dysplastic nevus” OR “clark nevus” OR “atypical nevus” OR “dysplastic nevi” OR “clark nevi” OR “atypical nevi”). Time span was set to 1992–2022. Document type was set to Article. Titles, authors, abstracts, institutions, countries, journals, references, and the citation information were recorded. Results Although the number of publications has declined over time, the USA remains the leading contributor to published articles. Key clusters of frequently used keywords were identified. The Journal of the American Academy of Dermatology had the highest number of published titles. Country and journal analysis were supplemented by co-citation and co-cited reference cluster analysis. Burst analyses revealed authors like Kittler, Argenziano, and Gandini as significant contributors, with their works receiving strong citation bursts extending until the end of the study period. Conclusions This bibliometric analysis revealed trends and interest pockets in the literature pertaining to dysplastic nevi and melanoma. This study aids in understanding the current research landscape and highlights potential future directions in this field.
This scientometric review takes 351 documents from 1992–2021 as the research object based on the Web of Science database. With the help of CiteSpace, this study aims to construct visualization mapping knowledge domains, display the research status in shadow education more intuitively, contribute opportunities for further research, and provide a more visual basis for dialog among researchers, policymakers and interested actors in the field. This study, by building coauthor, coword, and cocitation knowledge visualization maps, demonstrates cooperation among authors, research hotspots and frontiers in the field. Our results show that shadow education has experienced a rapid expansion over the last decade but that the scope of the collaborative circle of academia needs to be further expanded. Furthermore, because of shadow education’s variable forms, researchers need to pay extra attention to the scope of its definition. Parents are involved in too many of their children’s educational choices; learning requires more self-drive and improved self-learning ability.
This is an examination of the history and the state of the art of the quest for visualizing scientific knowledge and the dynamics of its development. Through an interdisciplinary perspective this book presents profound visions, pivotal advances, and insightful contributions made by generations of researchers and professionals, which portrays a holistic view of the underlying principles and mechanisms of the development of science. This updated and extended second edition:highlights the latest advances in mapping scientific frontiersexamines the foundations of strategies, principles, and design patternsprovides an integrated and holistic account of major developments across disciplinary boundariesAnyone who tries to follow the exponential growth of the literature on citation analysis and scientometrics knows how difficult it is to keep pace. Chaomei Chen has identified the significant methods and applications in visual graphics and made them clear to the uninitiated. Derek Price would have loved this book which not only pays homage to him but also to the key players in information science and a wide variety of others in the sociology and history of science. Eugene GarfieldThis is a wide ranging book on information visualization, with a specific focus on science mapping. Science mapping is still in its infancy and many intellectual challenges remain to be investigated and many of which are outlined in the final chapter. In this new edition Chaomei Chen has provided an essential text, useful both as a primer for new entrants and as a comprehensive overview of recent developments for the seasoned practitioner. Henry SmallChaomei Chen is a Professor in the College of Information Science and Technology at Drexel University, Philadelphia, USA, and a ChangJiang Scholar at Dalian University of Technology, Dalian, China. He is the Editor-in-Chief of Information Visualization and the author of Turning Points: The Nature of Creativity (Springer, 2012) and Information Visualization: Beyond the Horizon (Springer, 2004, 2006).
Clustering using cocitation and bibliographic coupling is an effective tool to analyze the structure of scientific research, but its arbitrariness in the setting of a clustering threshold is a problem. This study tracked modularity of citation networks in research domains, and we found that there are three stages in clustering of citation networks and it is universal across our case studies. In the first stage, core clusters in the domain are formed. In the second stage, peripheral clusters are formed, while core clusters continue to grow. In the third stage, core clusters grow again. By focusing on the elementary process in clustering, we can understand the structure of citation networks in each research domain and judge the location of each cluster and paper, which cannot be seen in the final clustering results.
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mech-anisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
The reward and communication systems of science are considered.