Social Networks 66 (2021) 10–25
Available online 31 January 2021
0378-8733/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Adoption and adaptation: A computational case study of the spread of
Granovetter’s weak ties hypothesis
*, 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
How do new scientic 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 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 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.
In the 1960s and 1970s, the question of how new scientic 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 scientic 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 communities—sometimes called “invisible col-
leges” (Crane, 1972) or “coherent groups” (Grifth and Mullins, 1972)—
in the organization of scientic research. Such communities develop
separate vocabularies and narratives through which their members
interpret scientic 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 scientic 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 scientic
ideas spread and change in the process of diffusion. This takes us away
from what Latour (1984) calls a “diffusion model” of 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: Granovetter’s (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-
tic idea was transformed during its spread as a result of the collective
behavior and interpretations of scholars. First, we trace the structural
spread of Granovetter’s hypothesis and analyze its macroscopic patterns
using a network representation of citation data. Next, we examine how
different communities in this diffusion network developed specic in-
terpretations of Granovetter’s 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: firstname.lastname@example.org (A. Keuchenius), email@example.com (P. T¨
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/socnet
Social Networks 66 (2021) 10–25
scientic 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 scientic 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 scholars—brokers and leaders—perform key roles in
translating and introducing the new scientic 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 scientic diffusion was laid in the
1960s and 1970s by scholars, such as Crane (1972), Goffman (1966),
Grifth and Mullins (1972), Merton (1968), Mulkay (1974), Price
(1963), and Small and Grifth (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 scientic world (Collins, 1983;
Crane, 1972; Grifth and Mullins, 1972; Price, 1963). This parallel be-
tween academic stars and opinion leaders in marketing is in part
inspired by Everett M. Rogers’s (1983) diffusion of innovations theory
and Elihu Katz’s 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
reafrmed 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-
ce—which 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 scientic 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 scientic 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 actors—not
of patients—and since the token is in everyone’s hands in turn,
everyone shapes it according to their different projects. This is why it
is called the model of translation.
(Latour 1984; p.267–268)
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, Greenhalg’s
(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 scientic inscriptions
such as diagrams are not “immutable” as Latour (1986) postulates—but
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 (Grifth, 1989; Small and
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 scientic ideas bifurcated, a divide
opened up between structural and interpretative approaches—the
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 the—structural—evolution in scientists networks on the one
hand and their—cultural—intellectual 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 scientic
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
scientic 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
network—the network that maps the spread of a particular innovation, in
this case Granovetter’s 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 Granovetter’s
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 Granovetter’s
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 scientic
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 network’s evolution
over time and identify the roles of key actors in brokering diffusion and
developing specic interpretations of the Strength of Weak Ties.
Data and methods
Our strategy is to apply network analysis to citation data of Gran-
ovetter’s 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, conrming 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 scientic idea, shaped by both structural and cultural forces.
This entails interest in the specics of interpretation and therefore re-
quires the type of ne-grained analysis enabled by the in-depth study of
a single case of scientic 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 scientic idea, and like qualitative
researchers, we rely on interpretative methods to develop a nuanced
understanding of qualitative differences in how Granovetter’s 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.
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 Granovetter’s 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 Grifth, 1974) relationships among
scholars. With our edges, we aim to capture the formal scientic
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 inuence
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 Science’s 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 Granovetter’s (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 difcult to gauge the extent of inuence 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 analysis—which
focuses upon the meso- or community level rather than upon micro-interactions
among scholars—it is sufcient to state that prior authors have ‘some inuence’
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 signicantly 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 identied through community detec-
tion). Finally, we do a close reading of key contributions to investigate
how the application and adaptation of Granovetter’s 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 inuential scholars within them.
To do so, we ran a temporal community detection algorithm to locate
communities in different time slices (1995–2000–2005–2010–2017)
(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 specic communities (Collins, 1998). In other words, their
leadership is not an individual property but emerges from the references
of numerous scholars in their communities—more 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 academics’ referencing 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 Granovetter’s hypothesis in various directions.
Before turning to the question of collaborative interpretation, however,
we rst need to ascertain that the network indeed exhibits signicant
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 congurations (>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, dened 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 signicant, we
need to compare its level of modularity with a plausible benchmark.
Since the structure of any network—and particularly networks with an
uneven degree distribution—will 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 Granovetter’s 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 scientic diffusion network would be struc-
tured, were it only organized by the timing of publications and scholars,
without scientic 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 signicantly
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
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 later—for example Brian Uzzi in 1999,
asi in 2000, and ¨
Orjan Bodin in 2006, respectively
twenty-six, twenty-seven, and thirty-three years after Granovetter’s
publication—but nevertheless receive many citations from the next
generation of adopters, making them important gures in the diffusion
of Granovetter’s hypothesis.
While these academic stars are cited by scholars in the entire
network, they are mostly—sometimes even exclusively—cited by
scholars from their own communities. These ndings show that the
spread of Granovetter’s 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 timing—as the rst-
mover advantage theory proposes—but 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).
Communities’ interpretative 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-
ovetter’s 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
4’s 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
scientic idea is mediated by scholarly communities—previously exist-
ing or newly formed—with 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 publications—identied
by the number of references they receive in their communities—is
necessary to better understand how scholars integrate Granovetter’s
hypothesis into their frameworks and apply it in their research. As we
shall see, Granovetter’s 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 Burt’s 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 Granovetter’s 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 Advantage” by 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
specically 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 individual’s
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
(Gephi’s 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
growth—in terms of community size—runs 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 Granovetter’s hypothesis varies signicantly: 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 benets 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 individual’s 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 ‘borrowed’ and useful to achieve ego’s 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 community’s main gure is Albert-L´
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
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 signicantly deviates from the overall network (two-sided Z-test) and which involve at least 5% of the community’s 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 community’s 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 quantiable emerging patterns. The rst
signicant 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
asi, Kimmo Kaski, Jari Saram¨
anos Kertesz, and
Jukka-Pekka Onella (e.g. Karsai et al., 2011; Onnela et al., 2007; Albert
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
work—cells, chemicals, and the Internet—speaks to the broad applica-
bility of Granovetter’s idea as interpreted by this community.
In contrast to the Organizational Advantage and Ego-Network
Communities, which reference both Granovetter’s 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 efciency 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 inefcient 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 efciency 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 scientic idea
in various directions. In the community examining organizational
advantage, weak ties are viewed as a collective organizational resource,
an antecedent and corollary of Burt’s 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 reects the development of
distinct research cultures which interpret and reuse Granovetter’s hy-
pothesis in different ways; and that most communities developed around
one or several central researchers active in spreading Granovetter’s 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
(1995–2000–2005–2010–2017) (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 hubs—for example Lin, Wellman, and Scott Feld—started 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. Breiger’s work is picked up by
scholars working in the—at that time—emergent New York School of
relational sociology (Emirbayer and Goodwin, 1994; Mische, 2011) who
use Breiger’s 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 dened communities in the diffusion network by the conguration of
edges in 2017. Our choice to use a static denition 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 researchers’ individual 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 interests—novelty versus contventionality, tradi-
tion versus innovation—become clearly visible if we compare Burt and
asi’s 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´
asi’s 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´
asi’s 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 Granovetter’s
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 inuenced 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 Burt’s 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.
Scientic 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 community’s cumulative knowledge. Through the lens
cultivated by the Organizational Advantage Community, we see that
whereas research communities provide a cultural context for re-
searchers’ scientic 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 Community’s 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 Granovetter’s hypothesis in diverging ways.
This computational case study has studied the process by which a
scientic idea is adopted and adapted as it spreads through scholarship,
focusing on the case of Granovetter’s (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 1995–2000–2005–2010–2017 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 reect Burt’s centrality in the overall network.
Where some individuals are very prominent within their cluster, Burt is inu-
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 hypothesis’ diffusion path generates
identiable scientic 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
specic 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 Granovetter’s 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 Burt’s 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´
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 ideas—a 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 Granovetter’s, and drawing upon his
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
inuential 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 https://doi.org/10.21942/uva.12310046.
This research received funding from the European Union’s 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 https://doi.org/10.1016/j.socnet.2021.01.001.
Abbott, A.D., 2001. Chaos of Disciplines. University of Chicago Press.
Adler, P.S., Kwon, S.-W., 2002. Social capital: prospects for a new concept. Acad. Manag.
Rev. 27, 17–40.
Albert, R., Barab´
asi, A.-L., 2002. Statistical mechanics of complex networks. Rev. Mod.
Phys. 74, 47–97.
Bail, C.A., 2014. The cultural environment: measuring culture with big data. Theory Soc.
asi, A.-L., Jeong, H., N´
eda, Z., Ravasz, E., Schubert, A., Vicsek, T., 2002. Evolution
of the social network of scientic collaborations. Phys. A: stat. Mech. Appl. 311,
Bettencourt, L.M.A., Kaiser, D.I., Kaur, J., Castillo-Ch´
avez, C., Wojick, D.E., 2008.
Population modeling of the emergence and development of scientic elds.
Scientometrics 75, 495–518.
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., 2008. Fast unfolding of
communities in large networks. J. Stat. Mech.: Theory Exp. P10008.
Bonifati, G., 2010. ’More is different’, exaptation and uncertainty: three foundational
concepts for a complexity theory of innovation. Econ. Innov. New Technol. 19,
Borgatti, S.P., Foster, P.C., 2003. The network paradigm in organizational research: a
review and typology. J. Manag. 29, 991–1013.
Brass, D.J., Galaskiewicz, J., Greve, H.R., Tsai, W., 2004. Taking stock of networks and
organizations: a multilevel perspective. Acad. Manag. J. 47, 795–817.
Breiger, R.L., Boorman, S.A., Arabie, P., 1975. An algorithm for clustering relational data
with applications to social network analysis and comparison with multidimensional
scaling. J. Math. Psychol. 12, 328–383.
Burt, R.S., 1984. Network items and the general social survey. Soc. Netw. 6, 293–339.
Burt, R.S., 1997. A note on social capital and network content. Soc. Netw. 19, 355–373.
Burt, R.S., 2000. The network structure of social capital. Res. Organ. Behav. 22, 345–423.
Burt, R.S., 2004. Structural holes and good ideas. Am. J. Sociol. 110, 339–349.
Campbell, K.E., Marsden, P.V., Hurlbert, J.S., 1986. Social resources and socioeconomic
status. Soc. Netw. 8, 97–117.
Centola, D., 2010. The spread of behavior in an online social network experiment.
Science 329, 1194–1197.
Centola, D., 2015. The social origins of networks and diffusion. Am. J. Sociol. 120,
Centola, D., Macy, M., 2007. Complex contagions and the weakness of long ties. Am. J.
Sociol. 113, 702–734.
Christakis, N.A., Fowler, J.H., 2007. The spread of obesity in a large social network over
32 years. N.Engl. J. Med. 357, 370–379.
Cole, S., 1970. Professional standing and the reception of scientic discoveries. Am. J.
Sociol. 76, 286–306.
Coleman, J., Katz, E., Menzel, H., 1957. The diffusion of an innovation among
physicians. Sociometry 20, 253–270.
Collins, H.M., 1983. The sociology of scientic knowledge: studies of contemporary
science. Annu. Rev. Sociol. 9, 265–285.
Collins, R., 1998. The Sociology of Philosophies: a Global Theory of Intellectual Change.
Harvard University Press, Cambridge, MA.
Crane, D., 1972. Invisible Colleges: Diffusion of Knowledge in Scientic Communities.
University of Chicago Press, Chicago.
Dahlander, L., McFarland, D.A., 2013. Ties that last: tie formation and persistence in
research collaborations over time. Adm. Sci. Q. 58, 69–110.
DiMaggio, P., Nag, M., Blei, D., 2013. Exploiting afnities between topic modeling and
the sociological perspective on culture: application to newspaper coverage of U.S.
Government arts funding. Poetics 41, 570–606.
Emirbayer, M., Goodwin, J., 1994. Network analysis, culture, and the problem of agency.
Am. J. Sociol. 99, 1411–1454.
Fisher, W.R., 1987. Human Communication as Narration: Toward a Philosophy of
Reason, Value, and Action. University of South Carolina Press, Columbia.
Flache, A., Macy, M.W., 1996. The weakness of strong ties: collective action failure in a
highly cohesive group. J. Math. Sociol. 21, 3–28.
Fortunato, S., Bergstrom, C.T., B¨
orner, K., Evans, J.A., Helbing, D., Milojevi´
Petersen, A.M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L.,
Wang, D., Barab´
asi, A.-L., 2018. Science of science. Science 359, eaao0185.
Foster, J.G., Rzhetsky, A., Evans, J.A., 2015. Tradition and innovation in scientists’
research strategies. Am. Sociol. Rev. 80, 875–908.
Fuhse, J.A., 2009. The meaning structure of social networks. Sociol. Theory 27, 51–73.
Fuhse, J.A., 2015. Theorizing social networks: the relational sociology of and around
Harrison white. Int. Rev. Sociol. 25, 15–44.
Goffman, W., 1966. Mathematical approach to the spread of scientic ideas: the history
of mast cell research. Nature 212, 449–452.
Goffman, W., Newill, V.A., 1964. Generalization of epidemic theory: an application to
the transmission of ideas. Nature 204, 225–228.
Goldberg, A., Stein, S.K., 2018. Beyond social contagion: associative diffusion and the
emergence of cultural variation. Am. Sociol. Rev. 83, 897–932.
Granovetter, M., 1985. Economic action and social structure: the problem of
embeddedness. Am. J. Sociol. 91, 481–510.
Granovetter, M.S., 1973. The strength of weak ties. Am. J. Sociol. 78, 1360–1380.
Greenhalgh, T., Robert, G., MacFarlane, F., Bate, P., Kyriakidou, O., Peacock, R., 2005.
Storylines of research in diffusion of innovation: a meta-narrative approach to
systematic review. Soc. Sci. Med. 61, 417–430.
Grifth, B.C., 1989. Understanding science. Commun. Res. 16, 600–614.
Grifth, B.C., Mullins, N.C., 1972. Coherent social groups in scientic change. Science
Hakimi, S.L., 1962. On realizability of a set of integers as degrees of the vertices of a
linear graph. I. J. Soc. Ind. Appl. Math. 10, 496–506.
Holton, G., 1962. Scientic research and scholarship notes toward the design of proper
scales. Sci. Technol. Contemp. Soc. 91, 362–399.
Kaiser, D.I., 2009. Drawing Theories Apart: The Dispersion of Feynman Diagrams in
Postwar Physics. University of Chicago Press.
Karsai, M., Kivel¨
a, M., Kumar Pan, R., Kaski, K., Kert´
esz, J., Barab´
asi, A.-L., Saram¨
2011. Small but slow World: how network topology and burstiness slow bown
spreading. Phys. Rev. E 83, 25102.
Katz, E., 1957. The two-step ow of communication: an up-to-date report on an
hypothesis. Public Opin. Q. 21, 61–78.
Kiss, I.Z., Broom, M., Craze, P.G., Rafols, I., 2010. Can epidemic models describe the
diffusion of topics across disciplines? J. Informetr. 4, 74–82.
Kleinberg, J.M., 1999. Authoritative sources in a hyperlinked environment. J. ACM 46,
Kleitman, D.J., Wang, D.-L., 1973. Algorithms for constructing graphs and digraphs with
given valences and factors. Discret. Math. 6, 79–88.
Knorr Cetina, K., 1981. The Manufacture of Knowledge: an Essay on the Constructivist
and Contextual Nature of Science. Pergamon, Oxford.
Knorr Cetina, K., 1999. Epistemic Cultures: How the Sciences Make Knowledge. Harvard
University Press, Cambridge, MA.
Kossinets, G., Watts, D.J., 2006. Empirical analysis of an evolving social network. Science
(New York, N.Y.) 311, 88–90.
Lambiotte, R., Panzarasa, P., 2009. Communities, knowledge creation, and information
diffusion. J. Informetr. 3, 180–190.
Lane, D.A., 2011. Complexity and innovation dynamics. Handbook on the Economic
Complexity of Technological Change. Edward Elgar Publishing, Cheltenham,
A. Keuchenius et al.
Social Networks 66 (2021) 10–25
Langville, A.N., Meyer, C.D., 2005. A survey of eigenvector methods for web information
retrieval. SIAM Rev. 47, 135–161.
Latour, B., 1984. The powers of association. Sociol. Rev. 32, 264–280.
Latour, B., 1986. Visualisation and cognition: drawing things together. Knowl. Soc.: Stud.
Sociol. Cult. Past Present 17, 813.
Latour, B., 1987. Science in Action: How to Follow Scientists and Engineers Through
Society. Harvard University Press, Cambridge, MA.
Latour, B., Woolgar, S., 1979. Laboratory Life: The Construction of Scientic Facts.
SAGE, Beverly Hills.
Levin, D.Z., Cross, R., 2004. The strength of weak ties you can trust: the mediating role of
trust in effective knowledge transfer. Manag. Sci. 50, 1477–1490.
Leydesdorff, L., Johnson, M., Ivanova, I., 2018. Toward a calculus of redundancy:
signication, codication, and anticipation in cultural evolution. J. Assoc. Inf. Sci.
Technol. 69, 1181–1192.
Leydesdorff, L., Petersen, A.M., Ivanova, I., 2017. Self-organization of meaning and the
reexive communication of information. Soc. Sci. Inf. 56, 4–27.
Lin, N., 1999. Social networks and status attainment. Annu. Rev. Sociol. 25, 467–487.
Lin, N., Dumin, M., 1986. Access to occupations through social ties. Soc. Netw. 8,
Lin, N., Ensel, W.M., Vaughn, J.C., 1981. Social resources and strength of ties: structural
factors in occupational status attainment. Am. Sociol. Rev. 46, 393.
Liu, W.T., Duff, R.W., 1972. The strength in weak ies. Am. Assoc. Public Opin. Res. 36,
Lyytinen, K., Damsgaard, J., 2011. Inter-organizational information systems adoption – a
conguration analysis approach. Eur. J. Inf. Syst. 20, 496–509.
Macy, M., 1991. Chains of cooperation: threshold effects in collective action. Am. Sociol.
Marsden, P.V., Campbell, K.E., 1984. Measuring tie strength. Soc. Forces 63, 482–501.
Martin, J.L., 2002. Power, authority, and the constraint of belief systems. Am. J. Sociol.
Merton, R.K., 1961. Singletons and multiples in scientic discovery: a chapter in the
sociology of science. Proc. Am. Philos. Soc. 105, 470–486.
Merton, R.K., 1968. The Matthew effect in science. Science 159, 56–63.
Mische, A., 2011. Relational sociology, culture, and agency. In: Scott, J., Carrington, P.
(Eds.), The SAGE Handbook of Social Network Analysis, vol. 1. Sage Publications
Sage UK, London, England, pp. 80–98.
Moed, H.F., 2005. What do references and citations measure? Citation analysis in
research evaluation, pp. 193–208.
Moody, J., 2004. The structure of a social science collaboration network: disciplinary
cohesion from 1963 to 1999. Am. Sociol. Rev. 69, 213–238.
Morgan, A.C., Economou, D.J., Way, S.F., Clauset, A., 2018. Prestige drives epistemic
inequality in the diffusion of scientic ideas. EPJ Data Sci. 7, 40.
Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P., 2010. Community
structure in time-dependent, multiscale, and multiplex networks. Science 328,
Mulkay, M., 1974. Conceptual displacement and migration in science: a prefatory paper.
Sci. Stud. 4, 205–234.
Mulkay, M.J., Gilbert, G.N., Woolgar, S., 1975. Problem areas and research networks in
science. Sociology 9, 187–203.
Nahapiet, J., Ghoshal, S., 1998. Social capital, intellectual capital, and the organizational
advantage. Acad. Manag. Rev. 23, 242–266.
Newman, M.E.J., 2001a. Scientic collaboration networks. I. Network construction and
fundamental results. Phys. Rev. E 64, 016131.
Newman, M.E.J., 2001b. Scientic collaboration networks. II. Shortest paths, weighted
networks, and centrality. Phys. Rev. E 64, 016132.
Newman, M.E.J., 2004a. Coauthorship networks and patterns of scientic collaboration.
Proc. Natl. Acad. Sci. USA 101, 5200–5205.
Newman, M.E.J., 2004b. Who Is the Best Connected Scientist?. A Study of Scientic
Coauthorship Networks. Springer, Berlin, pp. 337–370.
Newman, M.E.J., 2009. The rst-mover advantage in scientic publication. EPL
(Europhys. Lett.) 86, 68001.
Noyons, E.C.M., van Raan, A.F.J., 1998. Monitoring scientic developments from a
dynamic perspective: self-organized structuring to map neural network research.
J. Am. Soc. Inf. Sci. 49, 68–81.
Onnela, J.-P., Saram¨
aki, J., Hyv¨
onen, J., Szab´
o, G., Lazer, D., Kaski, K., Kert´
asi, A.-L., 2007. Structure and tie strengths in mobile communication
networks. Proc. Natl. Acad. Sci. USA 104, 7332–7336.
Pachucki, M.A., Breiger, R.L., 2010. Cultural holes: beyond relationality in social
networks and culture. Annu. Rev. Sociol. 36, 205–224.
Palla, G., Barab´
asi, A.-L., Vicsek, T., 2007. Quantifying social group evolution. Nature
Price, D.J.D.S., 1963. Little Science, Big Science. Columbia University Press, New York.
Price, D.J.D.S., 1965. Networks of scientic papers. Science 169, 510–515.
Price, D.J.D.S., 1976. A general theory of bibliometric and other cumulative advantage
processes. J. Am. Soc. Inf. Sci. 27, 292–306.
Rawlings, C.M., McFarland, D.A., Dahlander, L., Wang, D., 2015. Streams of thought:
knowledge ows and intellectual cohesion in a multidisciplinary era. Soc. Forces 93,
Reagans, R., McEvily, B., 2003. Network structure and knowledge transfer: the effects of
cohesion and range. Adm. Sci. Q. 48, 240.
Rogers, E.M., 1983. Diffusion of Innovations. Free Press, New York.
Rosvall, M., Axelsson, D., Bergstrom, C.T., 2009. The map equation. Eur. Phys. J. Spec.
Top. 178, 13–23.
Roth, C., Cointet, J.-P., 2010. Social and semantic coevolution in knowledge networks.
Soc. Netw. 32, 16–29.
Shi, F., Foster, J.G., Evans, J.A., 2015. Weaving the fabric of science: dynamic network
models of science’s unfolding structure. Soc. Netw. 43, 73–85.
Small, H., Grifth, B.C., 1974. The structure of scientic literatures I: identifying and
graphing specialties. Sci. Stud. 4, 17–40.
Strang, D., Meyer, J.W., 1993. Institutional conditions for diffusion. Theory Soc. 22,
Strang, D., Soule, S.A., 1998. Diffusion in organizations and social movements: from
hybrid corn to poison pills. Annu. Rev. Sociol. 24, 265–290.
Traag, V.A., 2015. Faster unfolding of communities: speeding up the Louvain algorithm.
Phys. Rev. E - Stat. Nonlinear Soft Matter Phys. 92, 032801.
Uzzi, B., Mukherjee, S., Stringer, M., Jones, B., 2013. Atypical combinations and
scientic impact. Science 342, 468–472.
Vilhena, D., Foster, J., Rosvall, M., West, J., Evans, J., Bergstrom, C.T., 2014. Finding
cultural holes: how structure and culture diverge in networks of scholarly
communication. Sociol. Sci. 1, 221–238.
Watts, D.J., 1999. Networks, dynamics, and the small-world phenomenon. Am. J. Sociol.
Watts, D.J., 2002. A simple model of global cascades on random networks. Proc. Natl.
Acad. Sci. 99, 5766–5771.
Way, S.F., Morgan, A.C., Larremore, D.B., Clauset, A., 2019. Productivity, prominence,
and the effects of academic environment. Proc. Natl. Acad. Sci. USA 166,
Wellman, B., Wortley, S., 1990. Different strokes from different folks: community ties
and social support. Am. J. Sociol. 96, 558–588.
White, H.C., Boorman, S.A., Breiger, R.L., 1976. Social structure from multiple networks.
I. Blockmodels of roles and positions. Am. J. Sociol. 81, 730–780.
Zeng, A., Shen, Z., Zhou, J., Wu, J., Fan, Y., Wang, Y., Stanley, H.E., 2017. The science of
science: from the perspective of complex systems. Phys. Rep. 714, 1–73.
Zilber, T.B., 2008. The work of meaning in institutional process and thinking. The SAGE
Handbook of Organizational Institutionalism. SAGE, London, pp. 151–169.
A. Keuchenius et al.