Toward a basic framework for Webometrics

Abstract

In this article, we define webometrics within the framework of informetric studies and bibliometrics, as belonging to library and information science, and as associated with cybermetrics as a generic subfield. We develop a consistent and detailed link typology and terminology and make explicit the distinction among different Web node levels when using the proposed conceptual framework. As a consequence, we propose a novel diagram notation to fully appreciate and investigate link structures between Web nodes in webometric analyses. We warn against taking the analogy between citation analyses and link analyses too far.

Full text

In this article, we define webometrics within the frame-
work of informetric studies and bibliometrics, as belong-
ing to library and information science, and as associated
with cybermetrics as a generic subfield. We develop a
consistent and detailed link typology and terminology
and make explicit the distinction among different Web
node levels when using the proposed conceptual frame-
work. As a consequence, we propose a novel diagram
notation to fully appreciate and investigate link struc-
tures between Web nodes in webometric analyses. We
warn against taking the analogy between citation analy-
ses and link analyses too far.
Introduction
Library and information science (LIS) and related fields
in the sociology of science and science and technology stud-
ies have developed a range of theories and methodologies—
now including webometrics—concerning quantitative as-
pects of how different types of information are generated,
organized, disseminated and used by different users in dif-
ferent contexts. Historically, this development arose during
the first half of the twentieth century from statistical studies
of bibliographies and scientific journals (Hertzel, 1987).
These early studies revealed bibliometric power laws like
Lotka’s law on productivity distribution among scientists
(Lotka, 1926); Bradford’s law on the scattering of literature
on a particular topic over different journals (Bradford,
1934); and Zipf’s law of word frequencies in texts (Zipf,
1949). Similar power-law distributions have been identified
on the Web, for example, the distribution of TLDs (top level
domains) on a given topic (Rousseau, 1997) or inlinks per
Web site (Adamic & Huberman, 2000, 2001; Albert, Jeong,
& Barabási, 1999).
Decisive for the development of bibliometrics and scien-
tometrics was the arrival of citation indexes of scientific
literature introduced by Garfield (1955) that enabled
analyses of citation networks in science (e.g., Price, 1965).
Access to online citation databases catalyzed a wide range
of citation studies, especially mapping scientific domains,
including growth, diffusion, specialization, collaboration,
impact, and obsolescence of literature and concepts. For
extensive coverage, see the ARIST chapters by White and
McCain (1989) and Borgman and Furner (2002).
The breakthrough of online citation analysis parallels the
later avalanche of webometric studies enabled by access to
large-scale Web data. In particular, the apparent yet ambigu-
ous resemblance between citation networks and the hyper-
textual interdocument structures of the Web triggered much
interest from the mid-1990s (e.g., Almind & Ingwersen,
1997; Bossy, 1995; Downie, 1996; Ingwersen, 1998; Kuster,
1996; Larson, 1996; McKiernan, 1996; Moulthrop &
Kaplan, 1995; Pitkow & Pirolli, 1997; Rousseau, 1997;
Spertus, 1997).
Furthermore, the central bibliometric measures of cocita-
tion (Small, 1973) and bibliographic coupling (Kessler,
1963) have been applied to studies of Web clustering, Web
growth, and Web searching (e.g., Ding, Zha, He, Husbands,
& Simon, 2001; Efe et al., 2000; Larson, 1996; Menczer,
2002; Pitkow & Pirolli, 1997; Weiss et al., 1996).
Since its advent, the Web has been widely used in both
formal and informal scholarly communication and collabo-
ration (e.g., Cronin, Snyder, Rosenbaum, Martinson, &
Callahan, 1998; Harter & Ford, 2000; Hurd, 2000; Thelwall
& Wilkinson, 2003; Wilkinson, Harries, Thelwall, & Price,
2003; Zhang, 2001). Webometrics thus offers potentials for
tracking aspects of scientific endeavor traditionally more
hidden from bibliometric or scientometric studies, such as
the use of research results in teaching and by the general pub-
lic (Björneborn & Ingwersen, 2001; Cronin, 2001; Thelwall
& Wilkinson, 2003; Thelwall, Vaughan, & Björneborn,
forthcoming) or the actual use of scientific Web pages.
A range of new terms for the emerging research field
were rapidly proposed from the mid-1990s, for example,
netometrics (Bossy, 1995); webometry (Abraham, 1996);
internetometrics (Almind & Ingwersen, 1996); webometrics
(Almind & Ingwersen, 1997); cybermetrics ( journal started
1997 by Isidro Aguillo)1; Web bibliometry (Chakrabarti,
Joshi, Punera, & Pennock, 2002). This and similar more
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 55(14):1216–1227, 2004
Toward a Basic Framework for Webometrics
Lennart Björneborn and Peter Ingwersen
Department of Information Studies, Royal School of Library and Information Science, DK 2300 Copenhagen
S–Denmark. E-mail: {lb, pi}@db.dk
Accepted January 23, 2004
© 2004 Wiley Periodicals, Inc. •Published online 13 August 2004 in Wiley
InterScience (www.interscience.wiley.com). DOI: 10.1002/asi.20077 1http://www.cindoc.csic.es/cybermetrics/

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004 1217
specific conceptual diversity and development often made
(and make) it difficult to understand what actually is ana-
lyzed in the contributions. The transformation over a year
from internetometrics to webometrics by the same authors,
Almind and Ingwersen (1996, 1997), is typical of the con-
ceptual confusion.
Tomas C. Almind wanted, originally, to investigate both
the communicative and networking aspects of the Internet
and to analyze the typology, contents, and characteristics of
the national Web pages, as in traditional bibliometric publi-
cation analyses. But it was unclear where the Internet stopped
and the Web started; hence the broad notion of internetomet-
rics in the original CIS Report (1996)2. However, because
Almind was very careful to distinguish between communica-
tion processes and contents, he and Ingwersen decided that
the publication analysis-like study published in 1997 were
entirely concerned with Web page types and properties—not
with communication on the Internet; hence the conception of
webometrics in the title of that classic article.
As a consequence of this conceptual variety, the present
paper proposes a consistent framework and terminology
with which to deal with matters of webometrics. The paper
is organized the following way. First, we set webometrics
and associated metrics into the LIS framework of informet-
rics. This is followed by an introduction of basic link termi-
nology and fundamental Web node diagram configurations.
The subsequent section is devoted to advanced link termi-
nology and Web node diagrams. The paper ends with a brief
discussion section and conclusions.
Webometrics, Bibliometrics, and Informetrics
Being a global document network initially developed for
scholarly use (Berners-Lee & Cailliau, 1990) and now in-
habited by a diversity of users, the Web constitutes an obvi-
ous research field for bibliometrics, scientometrics and
informetrics.
Webometrics and cybermetrics are currently the two most
widely adopted terms in library and information science for
this emerging research field. They are generically related,
see Figure 1, but often used as synonyms. In continuation of
the Almind case above, the present paper proposes a differ-
entiated terminology distinguishing between studies of the
Web and studies of all Internet applications. In this frame-
work, webometrics is defined as:
The study of the quantitative aspects of the construction and
use of information resources, structures and technologies
on the Web drawing on bibliometric and informetric
approaches. (Björneborn, 2004)
This definition thus covers quantitative aspects of both
the construction side and the usage side of the Web embrac-
ing four main areas of present webometric research: (1) Web
page content analysis; (2) Web link structure analysis;
(3) Web usage analysis (including log files of users’ search-
ing and browsing behavior); (4) Web technology analysis
(including search engine performance). This includes hybrid
forms, for example, Pirolli, Pitkow, and Rao (1996) who ex-
plored Web analysis techniques for automatic categorization
utilizing link graph topology, text content and metadata sim-
ilarity, as well as usage data. Further, all four main research
areas include longitudinal studies of changes on the dynamic
Web of, for example, page contents, link structures and
usage patterns. So-called Web archaeology (Björneborn &
Ingwersen, 2001) could in this webometric context be im-
portant for recovering historical Web developments, for ex-
ample, by means of the Internet Archive (www.archive.org).
The above definition places webometrics as a LIS spe-
cific term in line with bibliometrics and informetrics (also
cf., e.g., Cronin, 2001; Björneborn & Ingwersen, 2001). This
domain lineage is stressed by the formulation “drawing on
bibliometric and informetric approaches” because “drawing
on” denotes a heritage without limiting further methodolog-
ical developments of Web-specific approaches, including the
incorporation of approaches of Web studies in computer
science, social network analysis, hypertext research, media
studies, and so forth.
In the present framework, cybermetrics is proposed as a
generic term for:
The study of the quantitative aspects of the construction and
use of information resources, structures and technologies on
the whole Internet drawing on bibliometric and informetric
approaches. (Björneborn, 2004)
Cybermetrics thus encompasses statistical studies of
discussion groups, mailing lists, and other computer-
mediated communication on the Internet (e.g., Bar-Ilan, 1997;
Hernández-Borges, Pareras, & Jiménez, 1997; Herring,
2002; Matzat, 1998) including the Web. Besides covering all
computer-mediated communication using Internet applica-
tions, this definition of cybermetrics also covers quantitative
measures of the Internet backbone technology, topology,
and traffic (cf. Molyneux & Williams, 1999). The breadth
2Published by the now closed Centre for Informetric Studies (CIS) at the
Royal School of Library and Information Science, Denmark.
FIG. 1. Relationships between the LIS fields of infor-/biblio-/sciento-/
cyber-/webo-/metrics. Sizes of the overlapping ellipses are made for sake of
clarity only.

1218 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004
of coverage of cybermetrics and webometrics implies
large overlaps with proliferating computer-science-based
approaches in analyses of Web contents, link structures, Web
usage, and Web technologies. A range of such approaches
has emerged since the mid-1990s with names like cyber
geography and cyber cartography (e.g., Dodge, 1999; Dodge
& Kitchin, 2001, 2002; Girardin, 1995, 1996)3,Web ecology
(e.g., Pitkow, 1997; Chi et al., 1998; Huberman, 2001), Web
mining (e.g., Etzioni, 1996; Cooley, Mobasher, & Srivastava,
1997; Kosala & Blockeel, 2000), Web graph analysis (e.g.,
Broder et al., 2000; Clever Project, 1999; Kleinberg, Kumar,
Raghavan, Rajagopalan, & Tomkins, 1999), Web dynamics
(e.g., Levene & Poulovassilis, 2001), and Web intelligence
(e.g., Yao, Zhong, Liu, & Ohsuga, 2001).
The raison d’être for using the term webometrics in this
context could be to denote a close lineage to bibliometrics
and informetrics and stress a LIS perspective on Web studies
as noted previously. In this context, the earlier mentioned
term Web bibliometry used by Chakrabarti et al. (2002) is
especially interesting because computer scientists thus
recognize the heritage in bibliometric research to be drawn
on in Web studies. Other computer science approaches to
link structure analysis also pay tribute to inspiration from
citation studies, for example, Albert and Barabási (2002),
Chakrabarti et al. (1999), Efe et al. (2000), Kleinberg
(1999), Kosala and Blockeel (2000), Pitkow and Pirolli
(1997), Vázquez (2001).
There are different conceptions of informetrics, biblio-
metrics, and scientometrics. Figure 1 shows the field of in-
formetrics embracing the overlapping fields of bibliometrics
and scientometrics following widely adopted definitions by,
for example, Brookes (1990), Egghe and Rousseau (1990),
and Tague-Sutcliffe (1992). According to Tague-Sutcliffe
(1992, p. 1), informetrics is “the study of the quantitative
aspects of information in any form, not just records or
bibliographies, and in any social group, not just scientists.”
Furthermore, bibliometrics is defined as “the study of the
quantitative aspects of the production, dissemination and use
of recorded information” and scientometrics as “the study of
the quantitative aspects of science as a discipline or eco-
nomic activity” (ibid.). In the figure, politico-economical
aspects of scientometrics are covered by the part of the
scientometric ellipse lying outside the bibliometric one.
The diagram in Figure 1 further shows the field of webo-
metrics entirely encompassed by bibliometrics, because
Web documents, whether text or multimedia, are recorded
information (cf. Tague-Sutcliffe’s abovementioned defini-
tion of bibliometrics) stored on Web servers. This recording
may be temporary only, just as not all paper documents are
properly archived. Webometrics is partially covered by
scientometrics, as many scholarly activities today are Web-
based, while other such activities are even beyond biblio-
metrics, i.e., nonrecorded, like person-to-person conversa-
tion. Furthermore, webometrics is totally included within
the field of cybermetrics as defined previously.
In the diagram in Figure 1, the field of cybermetrics
exceeds the boundaries of bibliometrics, because some ac-
tivities in cyberspace normally are not recorded but rather
communicated synchronously, as in chat rooms. Cybermet-
ric studies of such activities still fit in the generic field of
informetrics as the study of the quantitative aspects of infor-
mation “in any form” and “in any social group” as stated
above by Tague-Sutcliffe (1992).
Naturally, the inclusion of webometrics expands the field
of bibliometrics, as webometrics inevitably will contribute
with further methodological developments of Web-specific
approaches. As ideas rooted in bibliometrics, scientometrics,
and informetrics contributed to the emergence of webomet-
rics, ideas in webometrics might now contribute to the
development of these embracing fields.
Terminology and Web Node Diagrams
The following three subsections deal with terminological
issues and forms of diagrams for conceptualizing and illus-
trating Web structures at different levels of analysis in a con-
sistent way.
Basic Link Terminology
The initial exploratory phases of an emerging field like
webometrics inevitably lead to a variety in the terminology
used. For example, a link received by a Web node (the net-
work term node here denotes a unit of analysis like a Web
page, directory, or Web site but could also be an entire top-
level domain of a country) has been named, for example,
incoming link, inbound link, inward link, back link, and
sitation; the latter term (McKiernan, 1996; Rousseau, 1997)
has clear connotations to bibliometric citation analysis. An
example of a more problematic terminology is the two op-
posite meanings of an external link: either as a link pointing
out of a Web site or a link pointing into a site.
Figure 2 illustrates an attempt to create a consistent basic
webometric terminology for link relations between Web
nodes (Björneborn, 2004). The figure reflects that the Web
may be viewed as a so-called directed graph, using a graph-
theoretic term (e.g., Broder et al., 2000; Kleinberg et al.,
1999). In such a Web graph, Web nodes are connected by
3Cf. http://www.cybergeography.org/
FIG. 2. Basic link relations (Björneborn, 2004). The letters may represent
different Web node levels, e.g., Web pages, Web directories, Web sites, or
top-level domains of countries or generic sectors. See legend in Table 1.

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004 1219
directed links. In this context, it should be noted that graph
theoretic approaches have been used in bibliometrics and
scientometrics since the 1960s for analyzing citation net-
works and other information networks (e.g., Egghe &
Rousseau, 1990; Furner, Ellis, & Willett, 1996; Garner, 1967;
Hummon & Doreian, 1989; Nance, Korfhage, & Bhat, 1972).
Social network analysis (e.g., Scott, 2000; Wasserman &
Faust, 1994) makes extensive use of graph theoretical ap-
proaches. Areview article by Park and Thelwall (2003) com-
pared information science approaches to studying the Web to
those from social network analysis. It was found that infor-
mation science tended to emphasize data validation and the
study of methodological issues, whereas social network
analysis suggested how its existing theory could transfer to
the Web. Otte and Rousseau (2002) give an excellent
overview of applications and potentials of social network
analysis in the information sciences with regard to studies of,
for example, citation and cocitation networks, collaboration
structures and other forms of social interaction networks,
including the Internet. In a forthcoming ARIST chapter on
webometrics by Thelwall, Vaughan, and Björneborn, appli-
cations of graph theory and social network analysis in webo-
metrics are further discussed. The proposed basic link termi-
nology in Table 1 has origins in graph theory, social network
analysis and bibliometrics.
The terms outlink and inlink are commonly used in
computer-science Web studies (e.g., Broder et al., 2000;
Chen, Newman, Newman, & Rada, 1998; Pirolli et al., 1996).
The term outlink implies that a directed link and its two ad-
jacent nodes are viewed from the source node providing the
link, analogous with the use of the term reference in biblio-
metrics. A corresponding analogy exists between inlink and
citation with the target node as the spectator’s perspective;
compare to Figure 3 (Björneborn, 2004). A link crossing a
Web site border, like link ein Figure 4, is thus called a site
outlink or a site inlink depending on the perspective of the
spectator. Similar considerations of consistent terminology
have been put forward in bibliometrics by, for example,
Price (1970) who emphasized a conceptual difference
between the reference and citation, which matches the dif-
ference between outlink and inlink just described.
The terms out-neighbor and in-neighbor in the proposed
terminology are also used in graph-theoretic Web research
(e.g., Chakrabarti et al., 2002). On the Web, self-links are
used for a wider range of purposes than self-citations in sci-
entific literature. This reflects a special case of the general
difference between outlinks/inlinks and references/citations.
Page self-links point from one section to another within the
same page. Site self-links (also known as internal links) are
typically navigational pointers from one page to another
within the same Web site.
Because of its dynamic and distributed nature, the Web
often demonstrates Web pages reciprocally linking to each
other—a case not normally possible in the traditional print-
based citation world. Reciprocal links, such as those be-
tween nodes E and F in Figure 2, is a widespread existing
Web term for mutual inlinks and outlinks between two Web
nodes. This reciprocity is not necessarily completely
symmetrical as there may be more links in one direction
between two Web nodes. Sometimes, reciprocal links may
be deliberately agreed by two Web site creators for attempt-
ing to obtain higher ranking in search engines employing
inlink counts in ranking algorithms as in Google (Brin &
Page, 1998; also cf. Walker, 2002).
In Figure 2, the triadically linked nodes D, E, and F cor-
respond to the social network analytic term triadic closure
(e.g., Skvoretz & Fararo, 1989), for example, used to denote
the probability that nodes D and F are transitively connected
if there are already links between D and E, and between
E and F. In social networks, such simple triadic structures
or triads are the building blocks of larger social structures
FIG. 3. Different link terminology for the same link depending on the
spectator’s perspective as denoted by the eyes (Björneborn, 2004).
FIG. 4. Simplified Web node diagram illustrating basic Web node levels
(Björneborn, 2004).
TABLE 1. Basic link terminology (Björneborn, 2004) for link relations in
Fig. 2.
•B has an inlink from A; B is inlinked; A is inlinking; A is an
in-neighbor of B.
•B has an outlink to C; B is outlinking; C is outlinked; C is an out-
neighbor of B.
•B has a self-link; B is self-linking.
•A has no inlinks; A is nonlinked.
•C has no outlinks; C is nonlinking.
•I has neither in- nor outlinks; I is isolated.
•E and F have reciprocal links; E and F are reciprocally linked.
•D, E, and F all have in- or outlinks connecting each other; they are
triadically interlinked.
•A has a transversal outlink to G: functioning as a shortcut.
•H is reachable from Aby a directed link path.
•C and D are colinked by B; C and D have co-inlinks.
•B and E are colinking to D; B and E have co-outlinks.
•Co-inlinks and co-outlinks are both cases of colinks.

1220 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004
(e.g., Scott, 2000; Wasserman & Faust, 1994). Milo et al.
(2002) use the term motif for similar simple triadic building
blocks of complex networks in general, for example, in
biochemistry, neurobiology, ecology, and engineering.
Most links on the Web connect Web pages containing
cognate topics (Davison, 2000). However, some links in
a Web node neighborhood may break such typical linkage
patterns and connect dissimilar topical domains. Such
(loosely defined) transversal links (Björneborn, 2001, 2004;
Björneborn & Ingwersen, 2001) function as cross-topic
shortcuts and may affect so-called small-world phenomena
on the Web. Small-world phenomena are concerned with
short distances along interconnection paths between nodes
in a network graph. For example, short distances between
two arbitrary persons through intermediate chains of ac-
quaintances of acquaintances as studied in social network
analysis (e.g., Milgram, 1967; Kochen 1989; Pool &
Kochen, 1978/1979), and popularized by the notion of “six
degrees of separation.” Watts and Strogatz (1998) intro-
duced a small-world network model characterized by highly
clustered nodes as in regular graphs, yet with short charac-
teristic path lengths between pairs of nodes as in random
graphs. In their seminal paper, Watts and Strogatz (1998)
showed that a very small percentage of long-range connec-
tions is sufficient in a small-world network to function as
shortcuts connecting distant nodes of the network.
The concepts of reachability and link paths as illustrated
in Figure 2 are both used in graph theory (e.g., Gross &
Yellen, 1999), for example, when describing small-world
properties as outlined previously.
The two colinked Web nodes C and D in Figure 2 with
co-inlinks from the same source node are analogous to
the bibliometric concept of cocitation (Small, 1973).
Correspondingly, the two colinking nodes B and E having
co-outlinks to the same target node are analogous to a bibli-
ographic coupling (Kessler, 1963). Colinks is proposed as a
generic term covering both concepts of co-inlinks and co-
outlinks. The underlying assumption for the use of both the
bibliometric and webometric concepts is that two documents
(or two authors/link creators) are more similar, i.e., more
semantically related, the higher the frequency of shared
outlinks (references) or shared inlinks (citations).
Basic Web Node Terminology and Diagrams
In webometric studies, it may be useful to visualize rela-
tions between different units of analysis, for example, in the
so-called Alternative Document Model (Thelwall, 2002;
Thelwall & Harries, 2003). Figure 4 shows a diagram illus-
trating some basic building blocks in a consistent Web node
framework (Björneborn, 2004). In the diagram, four basic
Web node levels are denoted with simple geometrical figures:
quadrangles (Web pages), diagonal lines (Web directories),
circles (Web sites), and triangles (country or generic top level
domains, TLDs). Sublevels within each of the four basic node
levels are denoted with additional borderlines in the corre-
sponding geometrical figure. For example, a triangle with a
double borderline denotes a generic second level domain
(SLD), also known as a sub-TLD, assigned by many coun-
tries to educational, commercial, governmental, and other
sectors of society, for example, .ac.uk, .co.uk, .ac.jp, .edu.au.
The simplistic Web node diagram in Figure 4 shows a
page Plocated in a directory of a subsite in a sub-TLD. The
page has a site outlink eto a page at a site in the same sub-
TLD. The outlinked page in turn is outlinking to a page at a
site in another sub-TLD in the same country. The link path
e-f-g ends at a page at a site in another TLD.
Zooming in on a single Web site, this may comprise several
subunits in the shape of subsites, sub-subsites, and so forth,
as indicated by hierarchically derivative domain names. For
example, as shown in Figure 5, the sub-subsite of The Image,
Speech and Intelligent Systems Research Group (isis.ecs.
soton.ac.uk) is located within the Department of Electronics
and Computer Science (ecs.soton.ac.uk), one of many sub-
sites at the University of Southampton, United Kingdom
(soton.ac.uk). Subsites and sub-subsites are denoted as circles
with double and triple borderlines, respectively. Subordinate
sublevels would logically be denoted with additional number
of borderlines. For sake of simplicity, the diagram does not
reflect actual numbers and sizes of elements.
Although some Web sites subdivide into derivative do-
main names, as shown previously, other Web sites locate the
same type of subunits into folder directories in their Web site
file hierarchy. Obviously, such diverse allocation and naming
practices complicate comparability in webometric studies. In
Figures 6A and 6B, one or more diagonal lines (resembling
URL slashes and reflecting the number of directory levels
FIG. 5. Simplified Web node diagram of a Web site containing subsites
and sub-subsites.
FIG. 6. Simplified Web node diagrams of a Web site and a subsite with
links between different directory levels including page subelements.

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004 1221
below the URLroot level) denote directories, subdirectories,
and so forth.
Web pages may also consist of subelements such as text
sections, frames, and so forth. Additional bands illustrate
such page subelements as in the targets of the page self-link
hand the page outlink ifrom the two sibling Web pages
in the same directory in Figure 6A. More numerous and
complex linkages within a site or subsite, and so forth, can
be illustrated by combinations of elements in Figures 6A and
6B, showing links between pages located either at different
directory levels (Figure 6A) or in sibling directories at the
same level (Figure 6B) in the Web site file hierarchies.
Naturally, any diagrammatic representation of large-scale
hypertext structures will get too tangled to be of any practi-
cal use or to be interpreted in any quantitative way. How-
ever, the proposed Web node diagrams with their simple and
intuitive geometrical figures are intended to be used to em-
phasize and illustrate qualitative differences between inves-
tigated Web node levels in a webometric study. Figure 7
shows an example of such a Web node diagram used to
illustrate included and excluded Web nodes and links in a
connectivity analysis of the UK academic Web space
(Björneborn, 2004). Moreover, the diagrams can illustrate
actual structural aspects of limited subgraphs of an investi-
gated Web space. Figure 8 gives an example of how the Web
node diagrams were used in the above study more specifi-
cally concerned with what types of links, pages, and sites
function as small-world connectors across dissimilar topical
domains in an academic Web space (Björneborn, 2004).
Advanced Link Terminology and Diagrams
The Web can be studied at different granularities employ-
ing what here will be called micro, meso, and macro level
perspectives (Björneborn, 2004). Micro level webometrics
consists of studies of the construction and use of Web pages,
Web directories, and small sub-subsites, and so forth, for
example, constituting individual Web territories. Meso level
webometrics is correspondingly concerned with quantitative
aspects of larger subsites and sites, and macro level webo-
metrics comprises studies of clusters of many sites, or fo-
cuses on sub-TLDs or TLDs. Several webometric studies,
including classic ones by Larson (1996) and Almind and
Ingwersen (1997), have used meso level approaches con-
cerned with site-to-site interconnectivity as well as macro
level TLD-to-TLD analysis, primarily applying page level
link counts. However, to extract useful information, links
may also be aggregated on different node levels as in the ear-
lier mentioned Alternative Document Model (Thelwall,
2002; Thelwall & Harries, 2003).
An adequate terminology for aggregated link relations
should capture both the link level under investigation and
the reach of each link. Such a terminology should reflect at
least three elements: (1) the investigated link level, (2) the
highest-level Web node border crossed by the link, and
(3) the spectator’s perspective (cf. Figure 3). For sake of
simplicity, the perspective from the outlinking nodes is
chosen in the following examples showing higher and higher
link aggregations.
FIG. 7. Example of Web node diagram illustrating qualitative differences between links and Web node levels in a webometric study. The figure illustrates
included and excluded Web nodes and links in an analysis of small-world link structures across the UK academic Web space (Björneborn, 2004). The bold
link AF symbolizes all included 207,865 page level links between 7,669 subsites at 109 different UK universities in the analysis. All other links were
excluded: AA(page self-links); AB (subsite self-links); AC and AD (site self-links); AE (site outlinks to university main sites); AG (site outlinks to ac.uk sites
outside data set); AH (sub-TLD outlinks, i.e., links to other UK sub-TLD); and AI (TLD outlinks, i.e., links to other TLD).

1222 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004
Figure 9 below shows 14 page level links including a
page level subsite outlink, kp(also being a page level site
self-link). The subscript in kpdenotes page level. If a webo-
metric study comprises just one level of links, the terminol-
ogy can be simplified to cover merely the link reach. In such
a case, lpis a site outlink, mpa sub-TLD outlink, and npa
TLD outlink.
For sake of simplicity, directory and subsite level links
will not be treated here. However, the terminology for these
levels would parallel the other levels included.
Figure 10 illustrates 11 site level links. For example, os
is a site level site outlink aggregating three page level
links from Figure 9. Site self-links are denoted with curved
arrows.
FIG. 8. Example of Web node diagram showing a limited subgraph. It contains an excerpt of shortest link paths (path length 4) between a subsite on eye
research (www.eye.ox.ac.uk) and a subsite in geography (www.geog.plym.ac.uk) to identify pages and sites that provide transversal (cross-topic) links across
dissimilar topical domains in the UK academic Web space (Björneborn, 2004). Bold links show one example of a shortest link path between the two men-
tioned subsites. Only links connecting subsites at different UK universities were considered (cf. Figure 7). See Appendix for affiliations.
FIG. 9. Web node diagram with page level links (Björneborn, 2004).

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004 1223
In this context, it should be noted that a site level link
always connects a source site with a target site. Correspond-
ingly, a page level link always connects a source page with
a target page; compare to Figures 8 and 9. This point is
necessary to make, because a target URL for a Web page
may deceivingly look like an URL for a Web site. It is thus
common Web practice to stem the target URL of top entry
pages of a Web site. For example, instead of writing the full
URL www.db.dk/default.htm in a target link pointing to the
top entry page of the Royal School of Library and Informa-
tion Science, it is more convenient to stem the URL to
www.db.dk because Web servers automatically look for
default pages for stemmed URLs. However, this stemmed
URL still denotes a Web page and not a Web site.
This line of higher and higher link aggregations ends with
sub-TLD level links as shown in Figure 11 and TLD level
links in Figure 12. Terminology for these levels parallel the
other levels included.
Discussion and Conclusion
We have demonstrated the relationships between the var-
ious metrics associated with library and information science
in the framework of its established subfield informetrics.
Most basically, we refer webometrics as belonging to cyber-
metrics and covered by an expanded concept of bibliomet-
rics. We believe that a general consensus exists as to this
framework within library and information science.
FIG. 10. Web node diagram with site level links.
FIG. 11. Web node diagram with sub-TLD level links. FIG. 12. Web node diagram with TLD level links.

1224 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—December 2004
and building blocks by which future discoveries and per-
spectives of the Web and webometrics hopefully will thrive.
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Appendix
Figure A1 shows a so-called path net consisting of all
shortest link paths (path length 4) between two subsites,
www.eye.ox.ac.uk and www.geog.plym.ac.uk, in a study of
small-world link structures across the UK academic Web space
(Björneborn, 2004). Only links connecting subsites at differ-
ent UK universities were considered in the study. ID numbers
refer to 7669 investigated subsites. Counts of page level links
between subsites are shown. White nodes denote subsites in-
cluded in the path net excerpt shown in Figure 8. The affilia-
tions of the subsites in the path net are listed in Table A1.
TABLE A1. The affiliations of the subsites in the path net.
Path net
level Id Short domain name Affiliation
0 1885 eye.ox.ac.uk Dept of Ophthalmology, Univ. of Oxford
1 102 medweb.bham.ac.uk School of Medicine, Univ. of Birmingham
1 913 fhis.gcal.ac.uk Faculty of Health, Glasgow Caledonian University
2 226 ilrt.bris.ac.uk Institute for Learning and Research Technology,
Univ. of Bristol
2 917 chem.gla.ac.uk Dept of Chemistry, Univ. of Glasgow
2 922 www2.arts.gla.ac.uk Faculty of Arts, Univ. of Glasgow
2 1812 bodley.ox.ac.uk Bodleian Library, Univ. of Oxford
2 1866 info.ox.ac.uk Official Oxford University web pages
2 2088 sci.port.ac.uk Faculty of Science, Univ. of Portsmouth
2 3017 scit.wlv.ac.uk School of Computing and Information Technology,
Univ. of Wolverhampton
3 1327 geog.le.ac.uk Dept of Geography, Univ. of Leicester
3 2540 homepages.strath.ac.uk Personal web pages, Univ. of Strathclyde
4 2068 geog.plym.ac.uk Dept of Geographical Sciences, Univ. of Plymouth
FIG. A1. Path net consisting of all shortest link paths between two subsites.