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Adoption and adaptation: A computational case study of the spread of Granovetter's weak ties hypothesis


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How do new scientific ideas diffuse? Computational studies reveal how network structures facilitate or obstruct diffusion; qualitative studies demonstrate that diffusion entails the continuous translation and transformation of ideas. This article bridges these computational and qualitative approaches to study diffusion as a complex process of continuous adaptation. As a case study, we analyze the spread of Granovetter's Strength of Weak Ties hypothesis, published in American Journal of Sociology in 1973. Through network analysis, topic modeling and a close reading of a diffusion network created using Web of Science data, we study how different communities in this network interpret and develop Granovetter's hypothesis in distinct ways. We further trace how these communities originate, merge and split, and examine how central scholars emerge as community leaders or brokers in the diffusion process.
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Social Networks 66 (2021) 10–25
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0378-8733/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (
Adoption and adaptation: A computational case study of the spread of
Granovetters weak ties hypothesis
Anna Keuchenius
*, Petter T¨
, Justus Uitermark
University of Amsterdam, Department of Sociology, Postbus 15578, 1001NB Amsterdam, The Netherlands
University of Amsterdam, Department of Geography, Postbus 15578, 1001NB Amsterdam, The Netherlands
Complex networks
Scientic communities
How do new scientic ideas diffuse? Computational studies reveal how network structures facilitate or obstruct
diffusion; qualitative studies demonstrate that diffusion entails the continuous translation and transformation of
ideas. This article bridges these computational and qualitative approaches to study diffusion as a complex process
of continuous adaptation. As a case study, we analyze the spread of Granovetters Strength of Weak Ties hy-
pothesis, published in American Journal of Sociology in 1973. Through network analysis, topic modeling and a
close reading of a diffusion network created using Web of Science data, we study how different communities in
this network interpret and develop Granovetters hypothesis in distinct ways. We further trace how these
communities originate, merge and split, and examine how central scholars emerge as community leaders or
brokers in the diffusion process.
In the 1960s and 1970s, the question of how new scientic ideas
diffuse was high on the agenda of science studies. Primarily using survey
methods, researchers at the time discovered some key dynamics in the
spread of ideas. They found that the diffusion of a scientic idea bears
similarities to the diffusion of other types of innovation, for example, in
that both follow an S-shaped growth curve (Crane, 1972; Holton, 1962;
Price, 1963; Mulkay et al., 1975). Research of this time also brought
attention to the role of interpretation in science: studies revealed the
central role of informal communitiessometimes called invisible col-
leges(Crane, 1972) or coherent groups(Grifth and Mullins, 1972)
in the organization of scientic research. Such communities develop
separate vocabularies and narratives through which their members
interpret scientic ndings (Fisher, 1987). While science studies of the
1960s and 1970s opened a new eld of research, scholars faced limita-
tions in their data and methods.
An explosive development in the availability of both data and so-
phisticated analytical techniques since the 2000s has reinvigorated the
eld of science studies, allowing researchers to study the development of
science at scale (Fortunato et al., 2018; Zeng et al., 2017). But compu-
tational analyses come with their own sets of research questions, since
they focus on the structural properties of scientic networks while
leaving the interpretative work to more qualitative researchers
(Pachucki and Breiger, 2010). Combining computational and interpre-
tative analyses in this article, we contend, can help reveal how scientic
ideas spread and change in the process of diffusion. This takes us away
from what Latour (1984) calls a diffusion modelof science, in which
researchers are passive nodes in a network through which ideas circu-
late, to what he calls a translation model,according to which re-
searchers shape the idea to their different projects, resulting in a
continuous transformation of the diffusant.
To enable an in-depth and systematic study of how ideas change as
they diffuse, we focus on a single idea that has diffused far and wide in
academia: Granovetters (1973) Strength of Weak Ties hypothesis,
published in American Journal of Sociology. We employ citation network
analysis, topic modeling and close reading to study the way this scien-
tic idea was transformed during its spread as a result of the collective
behavior and interpretations of scholars. First, we trace the structural
spread of Granovetters hypothesis and analyze its macroscopic patterns
using a network representation of citation data. Next, we examine how
different communities in this diffusion network developed specic in-
terpretations of Granovetters hypothesis and focus on the role of indi-
vidual scholars in this process.
Our work advances the literature in three ways. Theoretically, we
develop the notion of a diffusion network and conceptualize how
* Corresponding author.
E-mail addresses: (A. Keuchenius), (P. T¨
Contents lists available at ScienceDirect
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journal homepage:
Social Networks 66 (2021) 10–25
scientic innovations are variably adapted throughout their growth
trajectory. Our methodological contribution is to develop an approach
that bridges the gap between computational analysis of network prop-
erties and the interpretative analysis of meaning (cf. Fuhse, 2009;
Pachucki and Breiger, 2010). Finally, our substantive contribution is to
show that the spread of scientic ideas entails a complex process of
translation in which scholarly communities emerge as meso-level me-
diators, cultivating divergent interpretations of the diffusing idea in line
with the different research projects in which they are engaged. During
this process, some scholarsbrokers and leadersperform key roles in
translating and introducing the new scientic idea into their circles and
across academic boundaries.
The structure of our argument is as follows. The next section outlines
the gap between computational and interpretative approaches and
suggests how these two types of studies might be combined into an
interpretative computational approach. The subsequent section sum-
marizes our methods and explains how we used citation and publication
metadata to create a diffusion network. The following three sections
analyze: (1) the community structure of this network; (2) the interpre-
tative function of these communities; and (3) the evolution of commu-
nities over time, spurred by leading academics with different roles in the
diffusion process. The concluding section discusses the implications of
our case study for the diffusion of science.
Perspectives on the diffusion of science
The groundwork for the study of scientic diffusion was laid in the
1960s and 1970s by scholars, such as Crane (1972), Goffman (1966),
Grifth and Mullins (1972), Merton (1968), Mulkay (1974), Price
(1963), and Small and Grifth (1974). Their research demonstrates that
academics are organized in communities
that perform pivotal functions
in the diffusion and development of ideas. Scholarly communities tend
to be organized around one or several academic stars whose status is
reinforced through mechanisms of cumulative advantage (Merton,
1968; Newman, 2009; Price, 1976). These star researchers function in
their scholarly circles similarly to how opinion leaders function in
marketing: recognized as intellectual leaders by the community, they
serve as its representatives to the broader scientic world (Collins, 1983;
Crane, 1972; Grifth and Mullins, 1972; Price, 1963). This parallel be-
tween academic stars and opinion leaders in marketing is in part
inspired by Everett M. Rogerss (1983) diffusion of innovations theory
and Elihu Katzs concept of the two-step ow of communication, which
posits that innovations rst spread to opinion leaders, who in turn
spread them to consumers (Coleman et al., 1957; Katz, 1957).
While these early science scholars are often credited with formal-
izing the study of science through the development of mathematical
models (Goffman, 1966; Goffman and Newill, 1964; Merton, 1968;
Price, 1976), their work contains both quantitative and interpretative
insights by addressing the co-evolution of scholarly networks and
scholarly cultures. Since then, the study of diffusion has bifurcated. On
the one hand, computational scholars have leveraged the explosive
growth in the availability of data and sophisticated analytical methods
to study the structural properties of academic networks (Fortunato et al.,
2018; Zeng et al., 2017). On the other hand, institutional scholars and
more qualitatively minded researchers have emphasized the importance
of meaning and interpretation in science and diffusion (Knorr Cetina,
1999; Latour, 1987; Strang and Meyer, 1993). We discuss these research
trends separately before exploring how they might be brought into
Recent computational work on citation and co-author relations has
focused on uncovering the relational structures underpinning the
development of science and new discoveries (Fortunato et al., 2018). By
applying advanced methods to large digital datasets, this research has
reafrmed some of the ndings of earlier studies, including that science
is organized into communities (Lambiotte and Panzarasa, 2009; New-
man, 2001b, 2004a) that revolve around academic stars, who are more
likely to receive new references and engage in new collaborations
asi et al., 2002; Dahlander and McFarland, 2013; Newman,
2001b, 2004b). Fortunato et al. (2018) review these and other ndings
and outline a research eld they call SciSci”—the Science of Scien-
cewhich uses computational methods, large datasets, and modeling to
identify relational structures and mechanisms of discovery in science. A
key premise underlying this computational work is that science is a
complex system in which interactions on a microscopic level result in
non-linear dynamics and the emergence of unintended and unexpected
macroscopic patterns (Fortunato et al., 2018; Shi et al., 2015; Zeng et al.,
2017). In line with this premise, many scholars working in this eld do
not discriminate between social and natural systems. They adopt their
methods from the natural sciences, drawing parallels between the
diffusion of scientic knowledge and evolutionary processes or the
spread of diseases (Bettencourt et al., 2008; Goffman and Newill, 1964;
Kiss et al., 2010; Morgan et al., 2018; Zeng et al., 2017). Related
research uses agent-based simulations in which the behavioral patterns
of individuals are translated into simple rules for agents in the simula-
tion, such as adopt when more than three of my friends adopt,and
interactions between agents determine the speed and reach of diffusion.
A common research question in this eld is how different network
structures obstruct or facilitate diffusion (cf. Centola, 2015; Centola and
Macy, 2007; Watts, 2002). This kind of computational work, focused
exclusively on the structural aspects of diffusion, generally assumes that
the object of diffusion remains constant as it spreads.
At the same time, interpretative studies of the diffusion of science
have shown that the spread of scientic ideas entails not just adoption
but also adaptation, similar to re-invention (Rogers, 1983) or exaptation
(Bonifati, 2010). Knorr Cetina (1981) describes how the content of
knowledge depends on the different subcultures or epistemic commu-
nities in which it is practiced. Latour, 1984 similarly sets out how objects
and ideas take on different forms and meanings depending on the local
context in which they are adopted, and calls for a paradigm shift from
the diffusion model to a translation model (see also Latour and Woolgar,
1979). Latour describes the spread as a chain, with the diffusing idea as a
Each of the people in the chain is not simply resisting a force or
transmitting it in the way they would in the diffusion model: rather,
they are doing something essential for the existence and mainte-
nance of the token. In other words, the chain is made of actorsnot
of patientsand since the token is in everyones hands in turn,
everyone shapes it according to their different projects. This is why it
is called the model of translation.
(Latour 1984; p.267268)
In the translation model, not only does the spread come about as a
result of collective action, as described in the structural complexity
approach, it also involves adaptations of the idea as a consequence of the
interpretations and interactions of actors. More recently, Greenhalgs
(2005) study of the diffusion of the innovation paradigm shows that
different research traditions develop distinct stories and sometimes
contradictory interpretations of the same research ndings. Kaiser
(2009) examines the development of the Feynman Diagram in postwar
physics and illustrates how even the meaning of scientic inscriptions
such as diagrams are not immutableas Latour (1986) postulatesbut
A multitude of conceptualisations and operationalizations of community is
maintained in the literature. The generation of authors discussed here, pre-
dominantly looks at relational communities (Emirbayer and Goodwin, 1994),
with direct ties between scientists which are typically discovered by means of
survey data. However, these authors do not use the concept of community
strictly relational, simultaneously trying to get at the cognitive links between
members of the same community, see for example (Grifth, 1989; Small and
Grifth, 1974).
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
depend on the scholarly social circles in which it spreads. Theorists of
institutions such as Zilber (2008), Strang and Soule (1998), and Strang
and Meyer (1993) draw attention to the collaborative and interpretative
work involved in diffusion. Strang and Meyer (1993) consider diffusion
as a sense-making process in which actors must jointly construct an
understanding of a practice or idea before they can adopt it. In other
words, adoption requires adaptation and largely depends on the social
As the study of the diffusion of scientic ideas bifurcated, a divide
opened up between structural and interpretative approachesthe
former often made use of computational methods and large datasets, the
latter tended to be theoretical and privileged case studies. In study of
science, some efforts are made more recently to explore the interaction
between thestructuralevolution in scientists networks on the one
hand and theirculturalintellectual advancements (Moody, 2004) on
the other hand, theorizing on regularities in the patterns that unify these
two dimension (Abbott, 2001). Scholars in elds such as social network
analysis, information theory, opinion dynamics and relational sociology
have similarly sought to bridge this broader structural and cultural
chasm (Pachucki and Breiger, 2010). One such approach in social
network analysis investigates socio-semantic networks designed to
capture the joint dynamics of social and socio-semantic structures (Roth
and Cointet, 2010). Information theory scholars seek to expand their
frameworks to incorporate meaning into the analysis of scientic
communication (Leydesdorff et al., 2018, 2017). For instance, Vilhena
et al. (2014) nd that structural holes (cf. Pachucki and Breiger, 2010)
and cultural holes overlap but not coincide in science, underlining the
importance of studying not only citation networks but also the content of
scientic communication. In the elds of opinion dynamics and diffu-
sion modeling, disease as an analogy is under increasing criticism, as
scholars seek to incorporate meaning in previously structurally driven
models. For example, Goldberg and Stein (2018) advance a model based
on associative diffusion, in which the objects of diffusion are associa-
tions between beliefs and behaviors, showing how cultural differentia-
tion can arise without relying on structural fragmentation or homophily
among agents. Theoretical attempts at bridging the structural-cultural
divide in relational sociology have also been made. Martin (2002) ar-
gues for a formal investigation of the relation between beliefs and social
structure, while Fuhse (2009, 2015), building on the work of Harrison
White, systematically explores the meaning structure of social networks.
These contributions all provide clues as to how structural and inter-
pretive methods might be best combined to examine the co-evolution of
meaning and social relations.
We build on this literature by developing the notion of a diffusion
networkthe network that maps the spread of a particular innovation, in
this case Granovetters hypothesis on the Strength of Weak Ties, be-
tween adopters. Like scholars in Science of Science, we view the diffu-
sion of science as a complex process, and use computational methods
and citation-based diffusion networks to study its micro-macro dy-
namics. However, like interpretative scholars, we consider every cita-
tion to involve interpretation and adaptation, as Granovetters
hypothesis is inserted into particular narratives that aid researchers in
identifying and answering the questions of interest. As this process of
translation is the outcome of collective interpretative work, we hy-
pothesize that researchers self-organize into distinct diffusion commu-
nities. We are interested in the spreading patterns of Granovetters
hypothesis and how this idea is reinterpreted and adapted during the
diffusion process. Our main hypothesis is that diffusion networks are
comprised of structural communities that advance the same scientic
ideas in distinct ways. In addition to testing this general hypothesis, we
seek to understand what gives rise to these structural-cultural patterns in
the diffusion network. Accordingly, we examine the networks evolution
over time and identify the roles of key actors in brokering diffusion and
developing specic interpretations of the Strength of Weak Ties.
Data and methods
Our strategy is to apply network analysis to citation data of Gran-
ovetters hypothesis in order to identify structural patterns in diffusion
processes and then to use topic modeling and close reading of publica-
tions to understand the interpretative work scholars engage in. While
previous research examines aggregate knowledge ows between elds
or institutions, conrming the self-organization of science into com-
munities (Rawlings et al., 2015; Noyons and van Raan, 1998; Rosvall
et al., 2009), our interest is in the dynamics of the diffusion of a
particular scientic idea, shaped by both structural and cultural forces.
This entails interest in the specics of interpretation and therefore re-
quires the type of ne-grained analysis enabled by the in-depth study of
a single case of scientic diffusion. We thus conduct what might be
thought of as a computational case study. Like computational re-
searchers, we use advanced computational techniques to search for
relational structures in the spread of a scientic idea, and like qualitative
researchers, we rely on interpretative methods to develop a nuanced
understanding of qualitative differences in how Granovetters hypoth-
esis has been adapted by scholars in different communities.
To construct the diffusion network, we collected data on publications
referencing Granovetter (1973) from the Web of Science.
For each
publication, we retrieved the following metadata: author(s), title, jour-
nal, publication date, research areas, keywords, abstract, and references.
The dataset contains 8198 publications from May 1973 until November
2017. We used this data to construct a network that represents the
journey of Granovetters hypothesis through the academic landscape.
Previous studies on academic citation networks typically use edges to
represent either direct citation (Price, 1965), co-authorship (Newman,
2001a) or co-citation (Small and Grifth, 1974) relationships among
scholars. With our edges, we aim to capture the formal scientic
communication between authors that involved the idea in question. We
therefore combine both co-authorship and direct reference relations
between scholars, since both are signals that an exchange of ideas has
taken place between these scholars on the Strength of Weak Ties.
is, edges are drawn from scholars new to the Strength of Weak Ties
hypothesis to the scholars they cite who have previously used the hy-
pothesis, hence representing inuence
of prior authors (edge target) to
newly adopting authors (edge source). As we are interested more in the
spread of the idea than the intensity of its use, we only create outgoing
edges for publications in which authors reference Granovetter (1973) for
the rst time. Similarly, we draw directed edges of authors rst publi-
cation that references Granovetter (1973) to their co-authors on that
publication, on the assumption that co-authors work together to position
their work in relation to others, including Granovetter. For incoming
Although the Web of Sciences coverage is relatively broad, it primarily
includes publications from journals and contains fewer books and book
Reference and co-authorship relations might signal a different type of
communication about the diffusing idea. References might signal a simple in-
formation ow between weak ties in which the edge target informs the edge
source about the novel idea, similar to Granovetters (1973) study on job va-
cancies. A strong tie co-authorship relation might reveal more about how the
novel idea gets embedded in the literature and research methodology by the
edge target. However, both types of communication are integral parts of the
diffusion, are hard to discern and can take place in both types of relations. We
therefore do not discriminate between these two types of relations in our
It is difcult to gauge the extent of inuence of prior authors upon new
authors referencing the Strength of Weak Ties. Some scholars cite articles
without reading them; others use cited articles extensively (for an overview of
theories of citation, see, for example, Moed (2005)). For our analysiswhich
focuses upon the meso- or community level rather than upon micro-interactions
among scholarsit is sufcient to state that prior authors have ‘some inuence
over new authors.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
edges, in contrast to outgoing edges, we consider later publications. This
procedure generates a diffusion network that includes 8198 publica-
tions, 15,056 scholars (nodes), and 142,227 edges.
To determine whether communities indeed mediate the diffusion of
innovation, we rst test whether the modularity of the diffusion network
is signicantly higher than a random network with the same degree
distribution and sequence. We then use topic modeling to identify
principal themes and frames in the literature (Bail, 2014; DiMaggio
et al., 2013), and examine how these relate to the structural diffusion
communities in the network (as identied through community detec-
tion). Finally, we do a close reading of key contributions to investigate
how the application and adaptation of Granovetters hypothesis differs
between three large communities. To study how these
structural-cultural patterns emerge, we examine the development of
communities over time and the role of inuential scholars within them.
To do so, we ran a temporal community detection algorithm to locate
communities in different time slices (19952000200520102017)
(Mucha et al., 2010) and explore the paths of key gures in the diffusion
of the Strength of Weak Ties hypothesis. These key scholars play crucial
roles in the formation and linking of communities. They do not perform
this work on their own, but serve as focal points for scholars who
constitute specic communities (Collins, 1998). In other words, their
leadership is not an individual property but emerges from the references
of numerous scholars in their communitiesmore precisely, the com-
munities are formed through the references (Collins, 1998). Some
communities are quite closed and constructed around key scholars
important only to members of that community; other communities have
porous boundaries. By examining the role of these key scholars, we form
a better idea of the mechanisms by which diffusion communities are
constructed as a result of academicsreferencing practices.
Communities in the diffusion network
A key premise of our argument is that the diffusion network contains
clusters corresponding to communities of scholars who collaboratively
interpret and cultivate Granovetters hypothesis in various directions.
Before turning to the question of collaborative interpretation, however,
we rst need to ascertain that the network indeed exhibits signicant
clustering. We identify network communities using the Louvain algo-
rithm (Blondel et al., 2008; Traag, 2015), a community detection algo-
rithm which stochastically optimizes modularity. The Louvain
algorithm provides slightly different approximations of the optimal
partitions in different runs. To improve the robustness of our results, we
ran 10,000 instances of the algorithm and compared the resulting
community structures by focusing on scholars with a high indegree
(>200) (81 scholars representing 0.5% of the sample) and how they are
grouped together. We selected an instance where high indegree scholars
who are grouped together in the majority of congurations (>60% of 10,
000), are grouped together, and high indegree scholars who are never or
only rarely grouped together (<10% of 10,000) are not grouped
together, as an appropriately robust partition.
When we examine the community structure of the diffusion network
(Fig. 1), we see that it consists of communities of scholars, dened as
groups of scholars with more edges between members of the same
community than between members of different communities. We refer
to these communities as diffusion communities. Fig. 2 shows the dis-
tribution of the size of the diffusion communities, which is very uneven:
the three largest communities comprise 45% of all scholars in the giant
component; the largest twelve communities (size >200), 86% of all
scholars in the giant component. Our analysis focuses on these 12
To gauge whether this community structure is indeed signicant, we
need to compare its level of modularity with a plausible benchmark.
Since the structure of any networkand particularly networks with an
uneven degree distributionwill have some degree of modularity,
nding a plausible benchmark is essential. For this, we use an adjusted
version of the Havel-Hakimi graph (Hakimi, 1962; Kleitman and Wang,
1973). We compare the modularity of our empirical network to the
average modularity of 10,000 Louvain partitions of adjusted
Havel-Hakimi networks with an identical degree sequence as the
empirical network. We treat reciprocal and singular links separately and
match their degree sequences to create our adjusted graphs. This is
necessary as our network has notably few reciprocal links, which is not
the case in the regular Havel-Hakimi graph. By design and logic of the
diffusion network, earlier links are not reciprocated. Only scholars who
reference Granovetter (1973) for the rst time in a co-authored publi-
cation have a reciprocal link in the diffusion network.
The adjusted Havel-Hakimi graph serves as a benchmark for our
network, as it represents the hypothesis that the structures of these
networks are products of a rst-mover advantage (Newman, 2009),
positing that the rst publications and scholars in a new research area
receive citations at a much higher rate than later ones. This hypothesis is
modeled as follows: the network grows over time as more scholars
discover Granovetters idea. Each new generation of researchers cites
Granovetter as well as previous generations of scholars: the rst gen-
eration cites only Granovetter; the second cites Granovetter and the rst
generation; the third cites Granovetter and the rst two generations, and
so on. This is the process that the Havel-Hakimi algorithm represents: it
generates graphs by successively connecting nodes of the highest degree
to nodes of the second highest degree, ordering the remaining nodes by
degree from high to low, and repeating the process. The Havel-Hakimi
graph thus captures how a scientic diffusion network would be struc-
tured, were it only organized by the timing of publications and scholars,
without scientic communities playing any role in the diffusion process.
By comparing the Strength of Weak Ties diffusion network with the
adjusted Havel-Hakimi graphs, we nd that the former has signicantly
more community structure (0.62, p-value<0.001). Fig. 3 shows our
diffusion network on the right and a random instance of the Havel-
Hakimi graph on the left with identical degree distributions (both for
singular and reciprocal links), demonstrating a marked difference in
network modularity.
Comparing these networks points to another structural feature that
the rst-mover advantage model leaves out. Fig. 3 shows how scholars
with highest indegree are located at the center of the adjusted Havel-
Hakimi graph, whereas they are spread out over different commu-
nities in the Strength of Weak Ties diffusion network. Scholars with high
indegree are authors
of publications containing a reference to Gran-
ovetter (1973) that are often referenced by scholars new to the Strength
of Weak Ties. Examining the growth of communities and the indegree of
scholars over time (Fig. 4), we see that the rst-mover advantage does
not seem to drive the diffusion process. Numerous scholars cite Gran-
ovetter (1973) much laterfor example Brian Uzzi in 1999,
o Barab´
asi in 2000, and ¨
Orjan Bodin in 2006, respectively
twenty-six, twenty-seven, and thirty-three years after Granovetters
publicationbut nevertheless receive many citations from the next
generation of adopters, making them important gures in the diffusion
of Granovetters hypothesis.
While these academic stars are cited by scholars in the entire
network, they are mostlysometimes even exclusivelycited by
scholars from their own communities. These ndings show that the
spread of Granovetters idea was not a simple process of contagion, but
that scholarly communities containing key gures played an important
role in its diffusion to a broader scholarly audience. The distinctive
feature of high-indegree scholars may not be simply timingas the rst-
mover advantage theory proposesbut their status (Cole, 1970; Morgan
et al., 2018; Way et al., 2019) or their ability to apply an existing idea in
a novel context, so that it speaks to scholars in other research commu-
nities (Lane, 2011). The latter point is part and parcel of the idea that
References to publications are included in the network as edges to all au-
thors of the referenced work, not only to the rst author.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
innovation takes place throughout the diffusion process, and not just at
its initiation (Lyytinen and Damsgaard, 2011).
Communitiesinterpretative work: The development of
We applied topic modeling to the abstracts of publications in the
twelve largest communities and explored correlations between topics
and communities. The resulting correlation matrix in Fig. 5 shows the
degree to which scholars in the twelve communities discuss various
topics. It reveals that different communities do indeed apply Gran-
ovetters idea to different topics (Chi-squared =2057, df =154, p-val-
ue<0.15), albeit to different degrees. Communities addressing similar
topics tend to be more connected in the citation network (Pearson cor-
relation =0.23, p-value =0.06) (see Fig. 6). For example, Community
4s topics are similar to those of Community 11, and these two com-
munities are strongly connected in the diffusion network based on ci-
tations (see appendix for details on topics).
We nd that communities comprise distinct combinations of scholars
from different research elds (Chi-squared =177,432,451, df =1100,
p-value<0.15) (Fig. 7), with communities closer in their research in-
terests exhibiting stronger connections in the citation network (Pearson
correlation =0.39, p-value<0.001) (Fig. 8). We can get a sense of a
Fig. 1. The largest 12 communities of the diffusion network in 2017, containing 10,787 scholars and 121,132 edges. The nodes are colored by their community and
the scholars with highest indegree of each diffusion community are labeled. The labels are sized according to their indegree.
Fig. 2. Distribution of community sizes in the diffusion network, with a small
number of large communities and a large number of small communities. The
largest three and twelve communities consist 45% and 86.4% of all scholars in
the giant component of the diffusion network.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
given community simply by looking at topics and disciplinary back-
grounds (Fig. 9). Scholars in Community 9, for example, appear to be
active in the eld of communication science, discussing words associ-
ated with Topic 12, including information,” “online and media.
These ndings provide prima facie evidence that the diffusion of a
scientic idea is mediated by scholarly communitiespreviously exist-
ing or newly formedwith different disciplinary perspectives and
research interests. While correlations between topics and research elds
do not demonstrate that scholars only cite within their eld or that they
limit themselves to specialized topics, they do show how the diffusion of
a novel idea via citations is closely linked to its contextual understanding
and applications. While topic modeling provides us with the contours of
interpretative schemas, a close reading of key publicationsidentied
by the number of references they receive in their communitiesis
necessary to better understand how scholars integrate Granovetters
hypothesis into their frameworks and apply it in their research. As we
shall see, Granovetters 1973 article planted a seed for a number of
research avenues and understandings of the Strength of Weak Ties,
which have each developed and diverged during the diffusion process.
We now turn to a more detailed analysis of the three largest com-
munities in the diffusion network, which each leverage and develop
another use case and interpretation of the Strength of Weak Ties. We
refer to them as the Organizational Advantage Community, the Ego-
Network Community, and the Complex Networks Community.
Community 1. The Organizational Advantage Community
Granovetter (1973) points out that weak ties are more likely than
strong ties to be bridges between socially cohesive clusters, and suggests
they are therefore crucial for the ow of information. This observation is
taken up by the Organizational Advantage Community in the context of
management and organizations. Most scholars in this community pub-
lish in the elds of management and organization. The central scholar is
Ronald S. Burt, followed by Sumantra Ghoshal, Janine Nahapiet, Daniel
J. Brass, Bill McEvily, Rob Cross, Ray Reagans, Stephen P. Borgatti,
Seok-Woo Kwon, and Paul S. Adler (see Fig. 10 for the structural
development and position of scholars in this community).
The vast majority of empirical studies in this community use rm-
level data and focus on innovation-based competitive advantage for
organizations (e.g. Reagans and McEvily, 2003; Nahapiet and Ghoshal,
1998; Brass et al., 2004; Adler and Kwon, 2002; Burt, 2000). According
to scholars in this community, innovation occurs when extant knowl-
edge and experience are combined in new ways and they relate this to
the structural patterns within organizations: innovation and good ideas
are more likely to appear near structural holes where the knowledge of
different social collectives intersects (Burt, 2004). The Strength of Weak
Ties is a pillar of knowledge creation in this community, and is the basis
for Burts notion of structural holes: The structural hole argument
draws on several lines of network theorizing that emerged in sociology
during the 1970s, most notably, Granovetter (1973) on the Strength of
Weak Ties(Burt, 2000, p. 340). Burt thus interprets, adapts, and ex-
tends Granovetters notion of the Strength of Weak Ties so that it be-
comes relevant to a community of scholars who seek to understand why
some organizations, corporations, and managers have advantages over
As this Organizational Advantage Community grows, social capital
becomes its most central concept, understood as the sum of the actual
and potential resources embedded within, available through, and
derived from the network of relationships possessed by an individual or
social unit (Nahapiet and Ghoshal, 1998, p. 243). Social Capital, In-
tellectual Capital, and the Organizational Advantageby Nahapiet and
Ghoshal (1998) is the most frequently cited article by new adopters in
this community. While this resonates with the work of scholars such as
Robert Putnam and James Coleman, scholars in this community are
specically interested how social capital may confer organizational
advantages to corporations or managers. They link concepts such as
social capital and weak ties to notions like intellectual capital, knowl-
edge, and innovation, also drawing upon other works of Granovetter
such as his writing on embeddedness (Granovetter, 1985).
Community 2. The Ego-Network Community
In his 1973 article, Granovetter illustrates his theoretical argument
with empirical evidence about job attainment that shows individuals
more often nd jobs through weak ties than through strong ones.
Members of the Ego-Network Community build on this to conceptualize
weak ties as a type of individual asset which enhances this individuals
status in society. The majority of scholars in this community publish in
sociology and are interested in how different types of social relation-
ships can confer advantages to individuals, particularly in terms of status
(e.g. Lin et al., 1981; Lin, 1999; Campbell et al., 1986). This focus on
individuals corresponds to the main data source for these scholars,
namely surveys. The central gures in this community are Nan Lin, Peter
V. Marsden, Barry Wellman, and Karen E. Campbell. These scholars laid
the groundwork for this community in the 1970s and 1980s and some
are directly connected to Granovetter, such as his thesis supervisor Nan
Fig. 3. The Strength of Weak Ties diffusion
network in 2017 (right) and a random adjusted
Havel-Hakimi graph with identical degree dis-
tribution (for both reciprocal and singular
edges). Both visualizations have identical set-
tings, with nodes sized and colored by their
indegree and the same layout algorithm
(Gephis Force Atlas 2). The diffusion network
is more clustered (0.623 p-value<0.001) than
the adjusted HH graph. The high indegree
scholars are highly centered in the HH graph
and more spread out over different commu-
nities in the diffusion network.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
Fig. 4. The growth (line) and indegree of researchers (scatter) in each community of the Strength of Weak Ties diffusion network over time. The y-axis for
growthin terms of community sizeruns from 0 to 100%, but is not shown for the sake of legibility. The scatter points of the eight scholars with highest indegree
per community (and indegree>100) are labeled. Most communities have at least one important high indegree scholar, and the timing of these scholars rst
publication referencing Granovetters hypothesis varies signicantly: not all are rst movers.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
In this community, a central research topic is how individuals derive
different kinds of benets from strong and weak ties; some of its most-
cited publications are devoted to measuring tie strength using survey
questionnaires (Marsden and Campbell, 1984). The central concept of
this community is social resources: different kinds of ties offer
different kinds of support to individuals (Wellman and Wortley, 1990).
A central theoretical notion is the social resource proposition, explained
by Lin and Dumin (1986, p. 366) as: an individual who uses a contact of
higher socioeconomic status should nd a better job than someone else
whose contact has lower status.Scholars in this community likewise
explore the hypothesis that weak ties confer distinct advantages: for
two individuals at the same or similar initial positions, it is hypothesized
that the one who uses weak ties rather than strong ties will tend to reach
better social resources. This is called the strength of ties proposition
(Lin and Dumin, 1986, p. 367).
Ties are seen as an individuals property, as stated in the following
passage from one of the most cited publications in this community: The
friend may use his/her position or network to help ego to nd a job.
These are ‘borrowedand useful to achieve egos certain goal, but they
remain property of the friend or his/her friends (Lin, 1999, p. 468).
Whereas scholars researching organizational advantage nd strength in
weak ties by viewing them as a collective property, scholars studying
ego-networks consider weak ties as individual property that can
strengthen individual status.
Community 3. The Complex Networks Community
The Complex Networks Community shifts the focus from social
networks to networks in general. Granovetter (1973) presents the
Strength of Weak Ties as part of a broader argument for using structural
networks to link micro and macro levels of society. This ties in with the
central focus of this community: the study of complex networks, in
which individual properties and micro-interactions coalesce into
sometimes surprising macro-patterns. This community consists primar-
ily of physicists, science and technology scholars, and computer scien-
tists. The communitys main gure is Albert-L´
o Barab´
asi, a physicist
interested in detecting and modeling the universal properties of complex
Fig. 5. The topics (columns) addressed by communities (rows) in the Strength
of Weak Ties diffusion network. Cell numbers indicate coverage by all com-
munity publications, e.g. 36% of publications in the Complex Networks Com-
munity (community 3) address complex models (topic 11). The parameters for
topic modeling are set to nd 15 topics and to discard words that occur in less
than 30 articles or in more than 80% of articles. See appendix for details
of topics.
Fig. 6. The relation between communities in the Strength of Weak Ties diffusion network expressed by their direct citations (x-axis) vs. their similarity in topic
coverage (y-axis), Pearson correlation =0.23, p-value =0.06. The citation relation is calculated as the number of edges between communities a and b, divided by the
product of the sizes of communities a and b. The topic similarity is calculated as the correlation between the topics covered by communities a and b.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
Fig. 7. The disciplinary background of communities in the Strength of Weak Ties diffusion network. Each cell value and color represents the percentage of com-
munity researchers of a particular eld (e.g. 57% of researchers in community 4 publish on business & economics). The gure only contains research elds where at
least one community signicantly deviates from the overall network (two-sided Z-test) and which involve at least 5% of the communitys scholars.
Fig. 8. The relation between communities in the Strength of Weak Ties diffusion network expressed by their direct citations (x-axis) vs. their research areas (y-axis),
Pearson correlation =0.39, p-value =0.001. The citation relation is calculated as the number of edges between communities a and b, divided by the product of the
sizes of communities a and b. The research area similarity is calculated as the correlation between the research areas of communities a and b.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
Key words in the communitys dominant topic include model,
structure, nodes,” “properties,” “degree,and complex. The com-
munity is driven by data and models as it examines the structural pat-
terns of networks and quantiable emerging patterns. The rst
signicant scholars who formed this community include Duncan Watts,
Michael Macy, and Nicholas A. Christakis, whose work is partly situated
in sociology and links the behavior of individuals to collective behavior
and network characteristics (e.g. Centola and Macy, 2007; Centola,
2010; Christakis and Fowler, 2007; Kossinets and Watts, 2006). The
Strength of Weak Ties diffused from these more social science oriented
scholars towards physicists focused on numerical models, such as
o Barab´
asi, Kimmo Kaski, Jari Saram¨
aki, J´
anos Kertesz, and
Jukka-Pekka Onella (e.g. Karsai et al., 2011; Onnela et al., 2007; Albert
and Barab´
asi, 2002). This can be seen in Fig. 11, which shows the growth
of this community in the network. One of the most referenced works in
this community is Barab´
asi and Reka Albert (2002)s Statistical Me-
chanics of Complex Networks, which discusses abstract properties of
complex networks. The variety of environments considered in their
workcells, chemicals, and the Internetspeaks to the broad applica-
bility of Granovetters idea as interpreted by this community.
In contrast to the Organizational Advantage and Ego-Network
Communities, which reference both Granovetters 1973 article and his
work on economic life and embeddedness, the Complex Networks
Community almost exclusively references the 1973 article. The Strength
of Weak Ties idea is disconnected from a social setting and is instead
conceptualized as an efciency principle for diffusion processes in
complex networks. Damon Centola writes in his highly-cited article,
The Spread of Behavior in an Online Social Network Experiment:
Evidence in support of the Strength of Weak Ties hypothesis has sug-
gested that networks with high levels of local clustering and tightly knit
neighborhoods are inefcient for large-scale diffusion processes(2010,
p. 1197). Similarly, according to Barab´
asi et al. the Strength of Weak
Ties hypothesis states that the strength of a tie between A and B in-
creases with the overlap of their friendship circles, resulting in the
importance of weak ties in connecting communities. The hypothesis
leads to high betweenness centrality for weak links, which can be seen as
the mirror image of the global efciency principle(Onnela et al., 2007,
p. 7336). Consistent with an interest in emerging patterns, agent-based
simulations are the preferred method of inquiry among scholars in this
With a deeper understanding of the research interests of these three
communities (and of communities 4-12 in appendix), we see how
different communities of scholars translate and advance a scientic idea
in various directions. In the community examining organizational
advantage, weak ties are viewed as a collective organizational resource,
an antecedent and corollary of Burts notion of structural holes which
enables organizations to innovate. In the Ego-Network Community,
weak ties are considered individual property, most notably a resource
for individual status attainment. In the Complex Networks Community,
the Strength of Weak Ties is rst and foremost considered a universal
property of complex networks, independent of social context.
Emergence and growth of communities
Thus far, we have ascertained that our diffusion network has a
community structure; that this structure reects the development of
distinct research cultures which interpret and reuse Granovetters hy-
pothesis in different ways; and that most communities developed around
one or several central researchers active in spreading Granovetters idea
to new audiences. We now turn to the question of what gives rise to these
structural and cultural patterns. To do so, we examine the roles indi-
vidual researchers play as their work collectively shapes the diffusion
network over time.
To better understand the forces which shaped the diffusion network
over time, we require an historical analysis which considers changes in
communities over time.
We thus employ a temporal community
detection algorithm to nd communities in different time slices
(19952000200520102017) (Mucha et al., 2010), in which nodes in
each time slice are weakly linked to the other time slices (interslice
weight parameter =0.00001).
Fig. 12 shows the evolution of the communities over time (top) and
the community paths of key, highly cited scholars (bottom). Some of
these hubsfor example Lin, Wellman, and Scott Feldstarted out
belonging to different communities but later became part of the same
community, whereas Ronald Breiger and Burt belonged to the same
community and then split into different communities as they are
recognized for different contributions to the literature, diffusing the
Strength of Weak Ties to different audiences. Burt was acknowledged for
his ideas on structural holes within organizational networks (Burt, 1997,
2000, 2004), which became most popular among business and eco-
nomics scholars interested in innovation (the Organizational Advantage
Community). Breiger, alternatively, got known for his contributions on
mathematically identifying roles and positions in networks as matrices
(Breiger et al., 1975; White et al., 1976). Although his work builds less
explicitly on the Strength of Weak Ties, he acknowledges Granovetter,
who was also on his thesis committee. Breigers work is picked up by
scholars working in theat that timeemergent New York School of
relational sociology (Emirbayer and Goodwin, 1994; Mische, 2011) who
use Breigers concepts and algorithms for block model analysis. These
examples illustrate how the structural communities in the network are
Fig. 9. Size, central gures, prominent research elds and topics addressed by scholars in each community in the Strength of Weak Ties diffusion network. We have
named the topics to capture their essence, see appendix for more details of topics and for qualitative community descriptions.
While our analyses have been based on static characterizations of the data,
we seek to shed light on a complex and dynamic diffusion process. Thus far, we
have dened communities in the diffusion network by the conguration of
edges in 2017. Our choice to use a static denition of community was not only
technical, but an answer to the ontological question of what communities
represent in this case study: by using the full data from 2017, we apply the most
recent lens of history as the citation patterns of later researchers are used to
identify the community to which earlier contributions belong.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
related to the interpretative work of the scholars that constitute them
but also of scholars citing them at later points in time.
We see in these cases centrifugal forces that separate communities
and fragment the network, as well as centripetal forces that bring
together different communities, integrating the network. The nal
network structure is a balance of these opposing forces, which emerge
from researchersindividual behavior. As researchers navigate the ten-
sion between novelty and conventionality, they seek to create new
connections, while heeding the common practices of the discipline
needed for research to have impact (Foster et al., 2015; Uzzi et al.,
2013). When a new idea diffuses, researchers reinterpret it to introduce
insights into existing or developing traditions, thus acting as part of the
centrifugal force that strengthens the community while fragmenting the
larger network. Simultaneously, researchers use new ideas as links or
channels to other disciplines and bodies of literature, developing the-
ories that combine different ideas, thereby becoming part of the cen-
tripetal force that integrates and draws the diffusion network together.
These competing interestsnovelty versus contventionality, tradi-
tion versus innovationbecome clearly visible if we compare Burt and
asis roles in shaping the network. Barab´
asi references Granovetter
in a number of highly cited publications (Barab´
asi et al., 2002; Karsai
et al., 2011; Onnela et al., 2007), incorporating the Strength of Weak
Fig. 10. Growth of the organizational advantage community (community 1). All scholars in this community are colored green. Only scholars who received at least
250 citations from future adopters (indegree >=250) are labeled, sized according to indegree. The community develops around seminal works by central gures
such as Burt (1997, 2000, 2004), Nahapiet and Ghoshal (1998), Brass et al. (2004), Levin and Cross (2004), Reagans and McEvily (2003) and Borgatti and Foster
(2003). By 2005, all scholars to be most cited by this community have extensively referred to the Strength of Weak Ties. (For interpretation of the references to color
in this gure legend, the reader is referred to the web version of this article.)
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
Ties in a complex networks approach, leveraged by the Complex Net-
works Community. Although Barab´
asis star rises rapidly, he receives
citations almost exclusively (83%) from within his own Complex Net-
works Community (Fig. 13). As Fig. 11 shows, this community only took
off after 1999 and is primarily organized around Barab´
asis work, cited
by 43% of all new scholars in this community (Fig. 13). Like Barab´
Burt is prominent in the diffusion network, but his role is different. Burt
theorizes about structural holes and how brokerage enhances creativity
and innovation; he is not only the most prominent scholar in the
Organizational Advantage Community, but also the most central actor in
Fig. 11. Growth of the Complex Networks Community (community 3). All scholars referencing the Strength of Weak Ties before 2000, who might be considered
innovators in this community, are labeled irrespective of indegree. All scholars receiving at least 250 citations by future adopters (indegree >=250) are also labeled,
sized according to indegree. Temporal networks show how this community emerged slowly in 2000, spread due to scholars such as Macy (1991), Flache and Macy
(1996), Centola and Macy (2007) and Watts (1999), Kossinets and Watts (2006), and boomed after Albert and Barab´
asi (2002), Onnela et al. (2007), Palla et al.
(2007), Onnela et al. (2007) began citing Granovetter (1973).
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
the diffusion network as a whole (with the highest authority value
0.0047). Burt publishes many articles in which he cites Granovetters
Strength of Weak Ties publication (n=26) and offers contributions also
beyond the role of structural holes for organizational advantage, such as
insights on survey network data (Burt, 1984) and social capital (Burt,
1997). In his publications, he draws upon a wide variety of literature.
Burt is strongly connected to Ego-Network Community, having been
supervised by Lin for his M.A., and his ideas are much inuenced by his
doctoral advisor Harrison White, who features as central gure in
community 7, the community that works in the lines of relational soci-
ology. Burt receives a large number of citations (of 2.623 unique new
scholars in the network) and, in contrast to Barab´
asi, in notable amounts
by members of other communities than his own, see Fig. 13 for details.
Much of Burts earlier work has become canonical not only in man-
agement science and sociology, but also in the interdisciplinary eld of
network analysis. Like Granovetter, Burt advances ideas that nd their
way into publications on diverse topics with different theoretical un-
derpinnings and methodologies, in effect serving as a vehicle for
network integration. Burt thus diffuses the Strength of Weak Ties across
community borders, contributing to connecting networks of scholars.
Interestingly, Burt does what he theorizes: he is a broker operating
within the structural holes between communities in the academic
Our analysis demonstrates how researchers play different roles that
together generate countervailing forces which balance fragmentation
and integration in the diffusion network. This process is driven by the
work of key individuals, backed by collective citing behavior, that either
integrates a new idea into existing or developing specializations or lls
cultural and structural holes by connecting to other concepts and ideas.
Scientic communities are a cultural and structural fabric consisting of
strong ties between concepts and individuals, providing a context within
which researchers can develop their work and make novel contributions
that build on the communitys cumulative knowledge. Through the lens
cultivated by the Organizational Advantage Community, we see that
whereas research communities provide a cultural context for re-
searchersscientic work to have meaning, the weak ties between
research communities are where radical new ideas often emerge as a
variety of knowledge is combined in innovative ways (Burt, 2004). The
work of researchers is thus simultaneously and inextricably both cul-
tural and structural. Employing the lens of the Ego-Network Commu-
nity, scholars use both their knowledge and network as resources to
advance their work and academic status (Lin, 1999; Lin and Dumin,
1986). Drawing on the Complex Networks Communitys focus, we see
that they inadvertently fuel the centripetal and centrifugal forces, which
shape the cultural and structural network patterns we have analyzed in
this article: a diffusion network in which different structural commu-
nities interpret and apply Granovetters hypothesis in diverging ways.
This computational case study has studied the process by which a
scientic idea is adopted and adapted as it spreads through scholarship,
focusing on the case of Granovetters (1973) Strength of Weak Ties
Fig. 12. Temporal evolution of communities, detected with
the algorithm of Mucha et al. (2010), implemented by Vincent
Traag in the Louvain Python package, using interslice_weight
of value 0.00001 and 19952000200520102017 time sli-
ces. The alluvial diagram shows the largest 13 communities at
each time slice. Scholars in smaller communities and scholars
who have yet to reference the Strength of Weak Ties in each
time slice are omitted. The lower diagram shows the path of
important hubs and the splitting and merging of communities
over time, arising from both centrifugal and centripetal forces.
The authority value (Kleinberg, 1999; Langville and Meyer, 2005) measures
the centrality of a node by considering the centrality of its neighbors. The focus
here is on the incoming edges of nodes, hence the name authority centrality. His
high score on this measure thus reect Burts centrality in the overall network.
Where some individuals are very prominent within their cluster, Burt is inu-
ential across the diffusion network as a whole, connecting its different parts.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
hypothesis. We found that this hypothesisdiffusion path generates
identiable scientic communities, each of which develops its own
interpretation of the hypothesis. Scholars in the various communities
focus on different topics, ask different research questions, use distinct
vocabularies, and advance the hypothesis in particular ways that t into
their overall research framework. Central gures around whom com-
munities form play pivotal roles in this process; as scholars cite their
publications, their work locally becomes a focal point for both the cir-
culation and interpretation of the hypothesis.
Our analysis shows that a spreading idea is unlike viral diffusion or
social contagion in that every event of transmission involves interpre-
tation by the adopting scholar, consequently leading to a continuous
transformation of the idea. Like a chameleon adopting the colors of its
surroundings, the notion of weak ties takes on different guises, advanced
by the interests and perspectives of the scholars redeploying and
building on it. For some researchers, the Strength of Weak Ties is a
universal self-organizing principle of complex networks that is not
specic to any social context and can only be understood by considering
and modeling the network as a whole. Other scholars nd strength in
weak ties due to their ability to increase the relative status of individuals
in society, conceptualizing weak ties as an asset to an individual ego.
Different communities use the same reference to make very different
Looking at Granovetters original article on the Strength of Weak
Ties (1973), we can in retrospect see the potential for the different in-
terpretations which later emerged.
However, much like the varieties of
plants developing from the same seed, the idea progresses in diverging
directions as a result of interpretative actions and interactions of
numerous scholars. This process of developing distinct interpretations of
an idea functions structurally as a centrifugal movement in the diffusion
network, fragmenting and separating its communities. This is in line
with Burts intuition: good ideas come about by bridging structural holes
in social networks, but spread in ways that divide social groups (2004, p.
394). But we also identify centripetal forces in the diffusion process:
several scholars in the network actively work across different commu-
nities, tying together ideas and elds, thus integrating the network as a
In line with Latour (1984), this study suggests that translation,
Fig. 13. Citations to Ronald S. Burt (top) and Albert-L´
o Barab´
asi (bottom) from scholars in the twelve largest communities in the Strength of Weak Ties diffusion
network. The bars represent the percentage of scholars referencing publications by Burt or Barab´
asi on the rst occasion they refer to the Strength of Weak Ties. Burt
is highly cited in all communities. Barab´
asi is almost exclusively cited by scholars in his own community (by 43% of them).
In fact, there are traces of this in other literatures from that time as well, as
seems to be the case for most ideasa phenomenon also referred to as simul-
taneous invention or multiple independent discoveries (Merton, 1961). In 1972,
William Liu and Robert Duff published an article called The Strength in Weak
Ties, proposing an argument similar to Granovetters, and drawing upon his
doctoral thesis.
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
according to which both the circulation and the various meanings of an
idea result from numerous actions and interactions among individuals,
is a better model for the spreading of ideas than diffusion. Our meth-
odology captures both the structural, macroscopic patterns that arise as
a result of microscopic actions, namely diffusion communities centered
around local hubs, and the changes in meaning that follow from
numerous individual and collective interpretations and the development
of new lines of research. Our results illustrate how these structural and
cultural patterns are interrelated. We hope this will motivate researchers
to look for other methodologies and approaches that integrate these
insights and further our understanding of the mechanisms at play during
diffusion-translation processes, in science and beyond.
One open question is to what degree the diffusion communities
overlap with already existing scholarly communities or come about as a
consequence of the spread and research potential of new ideas. Another
avenue for future research would be to look deeper into the roles of
inuential scholars, to have a better sense of the extent to which they
perform unique translation work or receive credit for doing so because
of their status. With this article, we hope to suggest that further and
more sophisticated development of these ideas will require scholars of a
variety of methodological backgrounds.
Data availability statement
The data that support the ndings of this study are openly available
in gshare at
Funding statement
This research received funding from the European Unions Horizon
2020 research and innovation program under grant agreement No.
732942, project ODYCCEUS.
The authors thank Mark S. Granovetter, Loet Leydesdorff, Jan A.
Fuhse, Vincent Traag, Ludo Waltman and Marta Severo for helpful
comments on previous versions of this article.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in the
online version, at
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... larger Disparity i, t presents a lower network heterogeneity of the funds (Keuchenius et al., 2021). In this paper, we employ the Dependence i, t and Disparity i, t as independent variables to control the effect of the network characteristics. ...
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Identifying fundamental drivers of science and developing predictive models to capture its evolution are instrumental for the design of policies that can improve the scientific enterprise—for example, through enhanced career paths for scientists, better performance evaluation for organizations hosting research, discovery of novel effective funding vehicles, and even identification of promising regions along the scientific frontier. The science of science uses large-scale data on the production of science to search for universal and domain-specific patterns. Here, we review recent developments in this transdisciplinary field.
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The science of science (SOS) is a rapidly developing field which aims to understand, quantify and predict scientific research and the resulting outcomes. The problem is essentially related to almost all scientific disciplines and thus has attracted attention of scholars from different backgrounds. Progress on SOS will lead to better solutions for many challenging issues, ranging from the selection of candidate faculty members by a university to the development of research fields to which a country should give priority. While different measurements have been designed to evaluate the scientific impact of scholars, journals and academic institutions, the multiplex structure, dynamics and evolution mechanisms of the whole system have been much less studied until recently. In this article, we review the recent advances in SOS, aiming to cover the topics from empirical study, network analysis, mechanistic models, ranking, prediction, and many important related issues. The results summarized in this review significantly deepen our understanding of the underlying mechanisms and statistical rules governing the science system. Finally, we review the forefront of SOS research and point out the specific difficulties as they arise from different contexts, so as to stimulate further efforts in this emerging interdisciplinary field.
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Following a suggestion from Warren Weaver, we extend the Shannon model of communication piecemeal into a complex systems model in which communication is differentiated both vertically and horizontally. This model enables us to bridge the divide between Niklas Luhmann’s theory of the self-organization of meaning in communications and empirical research using information theory. First, we distinguish between communication relations and correlations among patterns of relations. The correlations span a vector space in which relations are positioned and can be provided with meaning. Second, positions provide reflexive perspectives. Whereas the different meanings are integrated locally, each instantiation opens global perspectives – ‘horizons of meaning’ – along eigenvectors of the communication matrix. These next-order codifications of meaning can be expected to generate redundancies when interacting in instantiations. Increases in redundancy indicate new options and can be measured as local reduction of prevailing uncertainty (in bits). The systemic generation of new options can be considered as a hallmark of the knowledge-based economy.
Faculty at prestigious institutions produce more scientific papers, receive more citations and scholarly awards, and are typically trained at more-prestigious institutions than faculty with less prestigious appointments. This imbalance is often attributed to a meritocratic system that sorts individuals into more-prestigious positions according to their reputation, past achievements, and potential for future scholarly impact. Here, we investigate the determinants of scholarly productivity and measure their dependence on past training and current work environments. To distinguish the effects of these environments, we apply a matched-pairs experimental design to career and productivity trajectories of 2,453 early-career faculty at all 205 PhD-granting computer science departments in the United States and Canada, who together account for over 200,000 publications and 7.4 million citations. Our results show that the prestige of faculty’s current work environment, not their training environment, drives their future scientific productivity, while current and past locations drive prominence. Furthermore, the characteristics of a work environment are more predictive of faculty productivity and impact than mechanisms representing preferential selection or retention of more-productive scholars by more-prestigious departments. These results identify an environmental mechanism for cumulative advantage, in which an individual’s past successes are “locked in” via placement into a more prestigious environment, which directly facilitates future success. The scientific productivity of early-career faculty is thus driven by where they work, rather than where they trained for their doctorate, indicating a limited role for doctoral prestige in predicting scientific contributions.
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