Social Network Theory

Abstract

Social network theory focuses on the role of social relationships in transmitting information, channeling personal or media influence, and enabling attitudinal or behavioral change. Since the 1960s, social network theory has significantly expanded the horizon of media effects research, with increasing application of network analytic methods in various empirical contexts. The two-step flow of communication hypothesis, the theory of weak ties, and the theory of diffusion of innovations are three major theoretical approaches that integrate network concepts in understanding the flow of mediated information and its effects.

Full text

Social Network Theory
WENLIN LIU, ANUPREET SIDHU, AMANDA M. BEACOM,
and THOMAS W. VALENTE
University of Southern California, USA
Research history and key concepts
A diverse array of research traditions has shaped the current state of social network
theory. As Scott (1991) summarizes, there are three lines of research that contributed
to the theory’s early development: the sociometric analysis tradition, which relies on
graph theory methods from mathematics; the interpersonal relations tradition, which
focuses on the formation of cliques among a group of individuals; and an anthropol-
ogy tradition that explores the structure of community relations in less developed
societies.
ese research traditions did not evolve into a coherent theoretical framework until
the 1960s. A number of sociologists signicantly advanced the social network approach
by synthesizing previous theoretical traditions and extending them to understand both
formal and informal social relations. For example, the sociometric view of social net-
works was elaborated, emphasizing structural properties, such as the relative location
of individual nodes in the network. Researchers during this time also advanced social
networktechniquesbyproposingblock modeling and multidimensional scaling.Block
modeling considers the particular position of a node in a social network. is method
enables researchers to identify nodes that have similar network positions, or what is
called structurally equivalent nodes. e scaling technique, on the other hand, allows
researchers to convert social relationships into sociometric distance,therebymapping
these relationships in a social space (Wasserman & Faust, 1994).
ree key network concepts that have organized research on network eects are
centrality, cohesion, and structural equivalence. Freeman (1979) proposed three dis-
tinct measures to indicate structural centrality: degree, closeness, and betweenness.
is seminal paper aorded a nuanced understanding of centrality, and it established
a process through which new network measures were developed to have a raw form,
a normalized form, and a network-level form. Freeman’s (1979) paper also motivated
subsequent research to assess how dierent forms of network centrality interact with the
ow of information dierently. For example, Borgatti’s simulation study (2005) identi-
ed a typology of ow processes, and he showed that the values of dierent central posi-
tions depend on the characteristics of the process (e.g., gossip diusion versus goods
delivery).
Network cohesion measures the degree of interconnections among a group of nodes.
is measure has long been useful to detect subgroups or cliques within the larger
e International Encyclopedia of Media Eects.
Patrick Rössler (Editor-in-Chief), Cynthia A. Honer, and Liesbet van Zoonen (Associate Editors).
© 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.
DOI: 10.1002/9781118783764.wbieme0092

2SOCIAL NETWORK THEORY
social network (Burt, 1987). In the context of media eects research, network cohesion
serves as an important structural feature that moderates the inuence of interpersonal
networks. Friedkin’s (1993) longitudinal study, among others, found that personal inu-
ence grows stronger within more cohesive social networks than less cohesive ones.
Finally, structural equivalence indicates two or more network positions that share a
similar pattern of connections with the rest of the network. Actors that occupy struc-
turally equivalent positions oen have similar characteristics, such as social status or
other individual traits. Because equivalent nodes are connected to a similar set of actors,
they are more likely to receive similar information or social inuence. In understanding
the process of diusion, Burt’s (1987) study found that innovations were more likely to
ow via structural equivalence than direct ties, suggesting equivalence inuence may
be a stronger predictor of behavioral adoption than cohesive inuence. Burt (1999)
further elaborated on these mechanisms to explain the role of opinion leaders in the
media eects context. He argued that there were two dierent network mechanisms at
play: a two-step ow process that consisted of opinion leaders spreading information to
the group, and a contagion process via structural equivalence that generated adoption
behaviors within the group.
e years since the 1990s have witnessed extensive applications of key network
concepts in diverse research contexts, and the eld has also constantly been updated
with more rened network measures and analytic tools. In the arena of media
eects research, the fundamental question is: How do social networks, including the
quality and quantity of relational ties, the structural position of individual actors in
anetwork,andtheoverallnetworkproperties(e.g.,itsdensity,centralization,and
modularity) impact the ow of media messages and their eects on the audience?
ese eects include public opinion formation, marketing, uses and gratications of
media consumption, and behavior change due to prosocial campaigns.
Although communication research did not substantially shape the initial develop-
ment of social network theory, there is an emerging trend of cross-pollination between
social network theory and media eects research. In large part, this cross-pollination
stems from the emergence of computer-mediated communication, which aords
explicit social networks as well as the modes of communication that bind them. e
following sections review three theoretical approaches that best represent the inuence
of social network theory.
Two-step flow of communication hypothesis
e two-step ow of communication hypothesis was rst proposed by Lazarsfeld,
Berelson and Gaudet in the book e People’s Choice (1944). In their study of voting
decisions, they found personal inuence, which was largely derived from people’s
social contacts and friendship networks, signicantly aected voting decisions. And
this eect was even more pronounced among people who were less committed to their
existing beliefs or who changed their minds during the course of a campaign. e
hypothesis is called two-step because the mass media initially inuence opinion leaders,
individuals who are perceived as inuential, who in turn inuence their social contacts.

SOCIAL NETWORK THEORY 3
Building on its initial formulation, Katz (1957) reviewed a number of corroborating
studies on this hypothesis and further elaborated on three important aspects of it.
First, the magnitude of personal inuence could be greater than that of mass media,
as rst identied in the 1940s voting study. Similar ndings also emerged from
subsequent cases, such as the Decatur Study, which examined individuals’ fashion
decision making (Katz & Lazarsfeld, 1955). Second, in terms of the ow of personal
inuence, opinion leaders are not always concentrated at certain social strata, nor
does personal inuence always ow from a higher social stratum to a lower one. On
the contrary, studies have observed many cases of local leadership or issue-specic
leadership. at is, leaders dier for dierent groups of people and leaders lead in
some domains but not others. Finally, personal inuence does not necessarily work in
isolation from mass media. e voting study revealed that opinion leaders tended to
be those who were more exposed to the mass media. And, depending on the specic
context, personal inuence can either reinforce or attenuate the eect of traditional
mass media.
Central to the two-step ow of communication process is the concept of opinion
leaders, a group of individuals inuential in specic domains. Numerous studies have
attempted to identify the key characteristics associated with being inuential along
three lines (Katz, 1957): who one is, the individual characteristics of the opinion leaders,
such as personality traits, charisma, or demographic and socioeconomic backgrounds;
what one knows, the characteristics pertaining to individuals’ competence, such as
their knowledge, expertise, or ability to provide information or guidance on particular
issues; and whom one knows, the characteristics related to an individual’s structural
position in a network. In other words, individuals may become opinion leaders
not only because they possess certain attributes but also because they occupy the
right network positions that enable them to eectively spread information and exert
personal inuence. Centrality measures have been particularly useful for identifying
leaders based on their network position.
As discussed, social network theory has proposed three types of network centrality
measures to identify the advantageous position that opinion leaders usually occupy:
degree, betweenness, and closeness (Freeman, 1979). Degree centrality measures the
number of links to and from an individual in a network. Individuals with high degree
centrality are more likely to become opinion leaders because more social ties can mean
greater opportunities to receive as well as disseminate information (see Figure 1, black
node). Betweenness centrality measures the frequency at which an individual node
lies on the shortest path connecting other nodes in the network. Individuals high in
betweenness centrality are more likely to serve as a bridge in the network—dened as a
node that connects otherwise unconnected network clusters. Just like gatekeepers in a
network, if individuals high in betweenness centrality oppose the dissemination of an
idea, this piece of information may not be able to ow to other areas of the network. In
Figure 1, the light gray node occupies this critical position. Finally, closeness central-
ity measures the average distance between an individual node and all other nodes in
the network. Individuals with higher closeness centrality need relatively fewer steps to
reach all other individuals in the network and thus can potentially move information
faster. e ability to eectively reach other contacts in one’s network makes individuals

4SOCIAL NETWORK THEORY
Opinion leaders with high
degree centrality
Opinion leaders with high
closeness centrality
Opinion leaders with high
betweenness centrality
Figure 1 Network illustration of opinion leaders with high degree centrality, closeness central-
ity, and betweenness centrality. Source: Adapted from Everett’s kite, in Brandes and Hildenbrand,
2014.
with high closeness centrality inuential. In Figure 1, the dark gray nodes have high
closeness centrality.
As one of the most applied theories in media eects research, the two-step ow of
communication hypothesis has been rigorously tested in various empirical settings.
e research on public opinion formation and agenda-setting, for instance, has
studied how inuential individuals, such as early recognizers of social issues, may
mediate the public and the media agendas by identifying emerging issues in the
media, diusing them among public audiences, and ultimately aecting the media
agenda (Brosius & Weimann, 1996). Research on health interventions has examined
thedualinuencesofinterpersonalnetworksandmassmedia.InValenteandSaba’s
(1998) study of a reproductive health communication campaign, they found both
the mass media campaign and personal networks were associated with individuals’
contraceptive adoption, but the impact of mass media was stronger for low threshold
individuals, those whose personal networks were composed of few contraceptive
users, than high threshold individuals, those whose networks contained a majority
of users.
Although the two-step ow hypothesis has been validated in numerous studies,
scholarship since the 1980s has pointed out that media inuence may take multiple,
recursive steps, and the overall process is more complex thana singular, one-directional
ow. With the rapid change in today’s media and communication environment, some
scholars also argue that the role of opinion leaders is becoming less pivotal. Bennett
and Manheim (2006), among others, have proposed that the traditional two-step ow
of media messages has gradually transformed into a one-step ow process, where

SOCIAL NETWORK THEORY 5
mass media are becoming more fragmented and niche media increasingly engage
in narrowcasting. Under this changed landscape, media messages may directly reach
their audience and opinion leaders thus would play a less signicant role than was
previously theorized.
Thetheoryofweakties
e theory of weak ties, articulated in Granovetter’s (1973) seminal piece “e Strength
of Weak Ties,” concerns the role of weak social ties in diusing ideas and informa-
tion. In his labor-market study, Granovetter observed that people more oen found jobs
through their weak social ties, as opposed to relying on their family or close friends. He
measured tie strength through the frequency of contact, asking respondents how oen
theysaweachcontactaroundthetimetheyacquiredthepieceofjobinformation.
In addition to contact frequency, studies have proposed a combination of factors to
indicate the strength of social ties, such as the duration of interaction, the amount of
eort individuals invest in a relationship, the extent to which the social ties provide
reciprocal utility (e.g., social support), and the level of intimacy exchanged in a relation-
ship. Based on these criteria, weak ties are generally dened as social relations requiring
little investment, and they are composed mostly of acquaintances or other loosely con-
nected actors, as opposed to kin or close friends.
Why are weak ties more likely to channel novel information than strong ties? To
explain the underlying mechanism of Granovetter’s ndings, it is necessary to return
to the network concept of bridging, mentioned previously in the denition of between-
ness centrality. Bridging ties are social connections that link two otherwise unconnected
network clusters. In other words, bridging ties provide the only path between two dis-
connected clusters, such as Cluster A and Cluster B in Figure 2. Granovetter found weak
ties were more likely to be bridging ties, because weak ties’ peripheral position made
them better able to reach outside information than strong ties. In Figure 2, imagine each
network cluster represents a circle of close friends, as all the nodes in each cluster are
connected to each other. In such highly interconnected circles, each person is likely to
Cluster A
Bridging tie
Cluster B
Figure 2 Bridging ties.

6SOCIAL NETWORK THEORY
receive a similar set of information. e bridging tie (sitting between the two clusters),
on the other hand, becomes the only opportunity for any nodes in Cluster A to access
novel information from Cluster B.
Although strong ties oen emerge from the center of a network, which gives
them greater capacity to diuse information and exert social inuence, Granovetter’s
thesis highlights the bridging function of weak ties and their ability to spread novel,
nonredundant information. e strength of weak ties, therefore, is not about the
numberofconnections.Rather,itliesinweakties’abilitytoreachabroader,and
potentially more heterogeneous, set of information sources. In the process of job
hunting, for example, the utility of strong ties diminishes, because they provide similar
and potentially redundant information to individuals.
Granovetter’sndingshaveledtoaseriesofreplicationstudies,suchasinthecon-
text of general information seeking, organizational knowledge sharing, the diusion
of innovations, community building, and many more. In the area of media eects,
studies have explored the role of weak discussion ties in promoting civic engagement.
For example, de Zúñiga and Valenzuela (2011) examined the association between
strong versus weak ties and individuals’ online and oine civic engagement. Among
factors such as discussion frequency and discussion network size, they found the
frequency of weak-tie discussion was the strongest predictor of individuals’ civic
behaviors.
e emergence of new media and social networking sites has created an increase in
online weak ties. Indeed, new media provide novel platforms through which individuals
can connect with geographically distant others, and functions such as “add friends,”
“follow the post,” “mention,” and “retweet” have been theorized as forms of weak social
ties. Some research has found evidence of online, mediated weak ties in maintaining
individuals’ bridging social capital, such as Ellison, Steineld, and Lampe’s (2007) study
on college students’ Facebook friends networks, whereas other scholars have argued
that online connections may breed slacktivism, as they fail to nurture meaningful civic
participation. e rise of new media platforms thus urges scholars to reconsider the
denition, conceptual boundary, and new typologies of weak ties. It also encourages
new research to examine the role of mediated weak ties in diusing information and
exerting social inuence.
Diffusion of innovations
e diusion of innovations occurs between individuals or organizations in a social
system.econnectionpatternbetweentheactorswhoinitiate,relay,andadoptinno-
vations can be viewed as a social network, where network connections may take the
form of friendship, advice, communication, or social support. e diusion process is
essentiallyanetworkedprocess.Asinnovationstravelthroughaninterconnectedweb
of social connections, the structure and characteristics of this network can determine
how widely and how soon the innovations get adopted (Valente, 1995).
InseveraleditionsofthebookDiusion of Innovations, Rogers (2010) formally intro-
duced the model for diusion and dened it as “the process in which an innovation

SOCIAL NETWORK THEORY 7
is communicated through certain channels over time among the members of a social
system” (p. 5). Rice (2011) dened the process of diusion in the context of media eects
“as the process through which an innovation (an idea, product, technology, process, or
service) spreads (more or less rapidly, in more or less the same form) through mass
and digital media, and interpersonal and network communication, over time, through
a social system, with a wide variety of consequences (positive and negative)” (p. 1).
e groundbreaking study in the eld of diusion was conducted by Ryan and
Gross in 1943 while they were investigating the diusion of hybrid corn seeds among
farmers in Iowa (see Valente, 1995). e early network approach of diusion studies
looked at how opinion leader status, indicated by the number of times an individual
was nominated as a network partner, was correlated with the time of adoption. Later
there emerged a more structural approach, and this approach shied the focus to
examine how the overall network pattern inuenced the adoption of innovations,
such as network density, the presence of weak ties (Granovetter, 1973) or structurally
equivalent positions (Burt, 1987), and so forth. Valente (1995) proposed a social net-
work threshold model, and this model is particularly characterized by its system-level
emphasis.
A large body of diusion studies has focused on identifying the factors and forces
that lead to the adoption of innovations among members of a certain population. ese
studies also aim to understand why some individuals or organizations adopt the inno-
vation sooner while others take more time to accept the same idea or practice. Current
scholarship has identied four main elements of the diusion model (Rogers, 2010;
Valente, 1995): the rate of adoption, which can be inuenced by the perceived char-
acteristics of the innovation and can be measured by mathematical models (Valente,
1995); the rate of adoption over time, which yields a cumulative S-shaped pattern; the
various stages during the adoption process, which can be further classied as knowl-
edge, persuasion, decision, implementation, and conrmation; and the modication of
the innovation.
In general, the adoption process entails learning about a new product, getting more
information about it, making a decision to adopt it or not, experimenting with it, and
eventually conrming the use of the product. e pace of diusion can be determined
by certain characteristics of the innovation, which include its: relative advantage, com-
patibility, complexity, trialability, observability, cost, and radicalness. Less radical, less
complex, and less expensive innovations, and innovations perceived as more advanta-
geous, compatible, trialable, and observable, spread more rapidly.
e ve adoption stages—knowledge, persuasion, decision, implementation, and
conrmation—have long been useful for understanding media eects and behavioral
change. Additional theories have further developed the adoption stages to evaluate
media campaign eects. For example, the hierarchy of eects model proposed 12
steps leading to behavioral change, and it estimated that individuals usually proceed
from one step to the next at a rate of 80% (Valente, 1995). e transtheoretical model
proposed specic cognitive stages of change—precontemplation, contemplation,
preparation, action, and maintenance—for quitting a behavior such as smoking
(Prochaska, DiClemente, & Norcross, 1992). It should be noted that a homogenous
model usually cannot capture the varying diusion processes in dierent contexts.

8SOCIAL NETWORK THEORY
Networkmodelshavebeendevelopedtomodelsocialinuenceprocesseswith
network weight matrices, such as relational, positional, and centrality measures, and
the weights based on social distance.
Diusion research peaked in the early 1960s, and it has been rekindled with the
rapid emergence of newer and more advanced network models and technology. e
application of diusion theory spans a wide array of disciplines, such as marketing,
economics, mathematics, sociology, anthropology, and epidemiology, among many
others. In the area of media eects research, the main premise of the theory is that
innovations enter into communities from external sources such as mass media or tech-
nological advancements, and then they spread via social networks and interpersonal
communication.
Mass media play a critical role in initiating diusion among opinion leaders and low
threshold adopters, as these individuals are more likely to rely solely on media infor-
mation to adopt an innovation (Figure 3). For opinion leaders or early adopters, their
initial decision of adoption may be independent of their social network ties. rough
processes such as the two-step ow, opinion leaders then spread the innovation to their
adjacent social network partners. At this stage, the early majority and late majority
may seek additional validating information to reduce uncertainty regarding the inno-
vation, from both traditional mass media and online media platforms such as Facebook
and Google. Toole, Cha, and González (2012) studied the spatiotemporal adoption of
Twitter, a microblogging web application, while also considering the interplay between
media and word of mouth. Media inuences at later stages of adoption increased the
Twitter user base twofold to fourfold. A study on news diusion across various social
media platforms analyzed over 386 million Web documents over a 1-month period in
2011. It found that, depending on issue domains, dierent types of media had varying
Cluster A
Bridging tie
Mass media
Cluster B
1
3
3
3
3
3
4
4
4
1
2
2
2
2
2
2
2
2
Figure 3 Two-step ow. 1: early adopter; 2: early majority; 3: late majority; 4: laggards.

SOCIAL NETWORK THEORY 9
degreesofinuence.Specically,socialnetworkingsitesandblogsweremostinuential
in politics and culture, news media in the arts and economics, social media in controver-
sial topics such as protests, and single social platforms in entertainment (Kim, Newth, &
Christen, 2013). erefore, media can inuence one’s perception of innovations as well
as one’s adoption behaviors.
Key contributions and future directions
Social network theory and methods oer a distinct perspective on and set of tools
with which to understand media eects, enabling consideration of how micro- and
macrosocial structures mediate and moderate media eects. e theories of two-step
ow and diusion of innovations examine the paths by which mediated messages travel
through social networks, and the concepts of opinion leadership and tie strength oer
insights into critical variables that aect this ow. While each of the theories discussed
here was developed in the twentieth century during the golden age of mass media tech-
nologies, their theoretical contributions endure as scholars continue to test and rene
them in an era of social media and rapid evolution in media technologies. ree direc-
tions for current and future research are highlighted below.
First, new media technologies such as social networking sites, microblogging tools,
and online recommendation systems oer intriguing opportunities for further appli-
cation and extension of social network theory in the study of media eects. Current
research in this area falls into two broad categories. One category investigates whether
and how network-based media eects theories such as diusion and the strength of
weak ties operate dierently in dierent forms of social, as opposed to mass, media.
Forexample,researchsuggeststhatsomeofthetraditionalsocialnetworkmeasuresof
opinion leadership discussed above may not be the best indicators of social inuence
on Twitter (Gruzd & Wellman, 2014). A second category of research capitalizes on the
large amount of and novel types of data available through social media to rigorously
test network-based media eects theories in ways not previously possible. For example,
largecorpusesofdigitaltracedatathatavoidpotentialself-reportbiasesofsurveydata
can be used to create randomized controlled experiments of the diusion of consumer
and political behavior on Facebook.
Second, media eects researchers have begun to extend social network theory and
methods beyond classic social contagion processes to engage in what Ognyanova and
Monge (2013) describe as a “relational reinterpretation” of numerous mass communi-
cation phenomena. Hyperlink networks, for example, in which the nodes are websites
and the ties are the hyperlinks that connect them, may be analyzed to trace the dif-
fusion of content between mainstream media and blogs, or to determine the extent to
which prominent mainstream media versus bloggers wield inuence in media and pub-
lic agenda-setting. Semantic networks, in which the nodes are words and the ties are
cooccurrences of those words in various media, may be mapped to identify patterns
in how content is framed across dierent outlets over time. ese network approaches
oer promising new methods for research on core media eects theories.

10 SOCIAL NETWORK THEORY
ird, ongoing advances in the statistical approaches used in social network analy-
sis promise continued improvement in the sophistication with which researchers are
able to model how social structure shapes or is shaped by media eects. In particular,
the development of models that allow for multiplex (multiple types of ties), multimode
(multiple types of actors), and multilevel networks enables consideration of greater
complexity in the study of diusion and mediated social inuence. ese developments
are particularly relevant in a new media environment in which actors may be both pro-
ducers and consumers (potentially necessitating multiplex ties), and people may access
content from many dierent types of sources and using many dierent types of media
(requiring multiple modes and levels). In sum, communication research has never been
more promising or relevant, and the theories introduced here oer insights into how to
move communication research forward.
SEE ALSO: Agenda-Setting: Individual-Level Eects Versus Aggregate-Level Eects;
Diusion eories: Logic and Role of Media; Diusion eories: Media as Innovation;
Diusion eories: News Diusion; Multistep Flow of Communication: Evolution of
the Paradigm; Multistep Flow of Communication: Network Eects; Multistep Flow of
Communication: Online Media and Social Navigation; Multistep Flow of Communica-
tion: Opinion Leadership and Personality Strength; Network Society: Networks, Media,
and Eects; Social Networking
References
Bennett, W. L., & Manheim, J. B. (2006). e one-step ow of communication. Annals
of the American Academy of Political and Social Science,608(1), 213–232. doi: 10.1177/
0002716206292266
Borgatti, S. P. (2005). Centrality and network ow. Social Networks,27(1), 55–71. doi: 10.1016/
j.socnet.2004.11.008
Brandes, U., & Hildenbrand, J. (2014). Smallest graphs with distinct singleton centers. Network
Science,2(3), 416–418. doi: 10.1017/nws.2014.25
Brosius, H. B., & Weimann, G. (1996). Who sets the agenda: Agenda-setting as a two-step ow.
Communication Research,23(5), 561–580. doi: 10.1177/009365096023005002
Burt, R. S. (1987). Social contagion and innovation: Cohesion versus structural equivalence.
American Journal of Sociology,92(6), 1287–1335. doi: 10.1086/228667
Burt, R. S. (1999). e social capital of opinion leaders.Annals of the American Academy of Polit-
ical and Social Science,566(1), 37–54. doi: 10.1177/0002716299566001004
de Zúñiga, H. G., & Valenzuela, S. (2011). e mediating path to a stronger citizenship: Online
and oine networks, weak ties, and civic engagement. Communication Research,38(3),
397–421. doi: 10.1177/0093650210384984
Ellison, N. B., Steineld, C., & Lampe, C. (2007). e benets of Facebook “friends”: Social capital
and college students’ use of online social network sites. Journal of Computer-Mediated Com-
munication,12(4), 1143–1168. doi: 10.1111/j.1083-6101.2007.00367.x
Freeman, L. (1979). Centrality in social networks: Conceptual clarication. Social Network,1,
215–239. doi: 10.1016/0378-8733(78)90021-7
Friedkin, N. E. (1993). Structural bases of interpersonal inuence in groups: A longitudinal case
study. American Sociological Review,58(6), 861–872. doi: 10.2307/2095955

SOCIAL NETWORK THEORY 11
Granovetter, M. S. (1973). e strength of weak ties. American Journal of Sociology,78(6),
1360–1380. doi: 10.1086/225469
Gruzd, A., & Wellman, B. (Eds.). (2014). Networked inuence in social media [Special issue].
American Behavioral Scientist,58(10), 1251–1259. doi: 10.1177/0002764214527087
Katz, E. (1957). e two-step ow of communication: An up-to-date report on a hypothesis.
Public Opinion Quarterly,21(1), 61–78. doi: 10.1086/266687
Katz, E., & Lazarsfeld, P. F. (1955).Personal inuence: e part played by people in the ow of mass
communication.Glencoe,IL:FreePress.
Kim, M., Newth, D., & Christen, P. (2013). Modeling dynamics of diusion across hetero-
geneous social networks: News diusion in social media. Entropy,15(10), 4215–4242. doi:
10.3390/e15104215
Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1944). e people’s choice: How the voter makes up
hismindinapresidentialcampaign. New York, NY: Columbia University Press.
Ognyanova, K., & Monge, P. (2013). A multitheoretical, multilevel, multidimensional network
model of the media system: Production, content, and audiences. In E. L. Cohen (Ed.), Com-
munication yearbook 37 (pp. 67–94). New York, NY: Routledge.
Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of how people
change: Applications to addictive behaviors. American Psychologist,47(9), 1102–1114. doi:
10.1037/0003-066x.47.9.1102
Rice, R. (2011). “Diusion of innovations.” Oxford Bibliographies Online: Communication.
Retrieved March 3, 2016, from http://www.oxfordbibliographies.com/view/document/obo-
9780199756841/obo-9780199756841-0045.xml
Rogers, E. M. (2010). Diusion of innovations (4thed.).NewYork,NY:FreePress.
Scott, J. (1991). Social network analysis: A handbook.London,UK:Sage.
Toole, J. L., Cha, M., & González, M. C. (2012). Modeling the adoption of innovations in the
presence of geographic and media inuences. PLOS ONE,7(1), e29528. doi: 10.1371/jour-
nal.pone.0029528
Valente, T. W. (1995). Network models of the diusion of innovations. Cresskill, NJ: Hampton
Press.
Valente, T. W., & Saba, W. P. (1998). Mass media and interpersonal inuence in a reproduc-
tive health communication campaign in Bolivia. Communication Research,25(1), 96–124. doi:
10.1177/009365098025001004
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.Cambridge,
UK: Cambridge University Press.
Further reading
Burt, R. S. (2000). e network structure of social capital. Research in Organizational Behavior,
22, 345–423. doi: 10.1016/s0191-3085(00)22009-1
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry,40(1),
35–41. doi: 10.2307/3033543
Granovetter, M. S. (1983). e strength of weak ties: A network theory revisited. Sociological
eory,1, 210–233. doi: 10.2307/202051
Valente, T. W. (2010). Social networks and health: Models, methods, and applications.NewYork,
NY: Oxford University Press.
Weimann, G. (1982). On the importance of marginality: One more step into the two-step ow
of communication. American Sociological Review,47, 764–773. doi: 10.2307/2095212

12 SOCIAL NETWORK THEORY
Wenlin Liu is a doctoral candidate at the Annenberg School for Communication,
University of Southern California, USA. Her research interests lie at the intersection
of interorganizational communication and social network theory and methodology.
Wenlin is a research member of the Center for Applied Network Analysis, led by
Dr. omas W. Valente.
Anupreet Sidhu is a doctoral student at the Department of Preventive Medicine, Uni-
versity of Southern California, USA. Her research interests lie in the area of health
campaign evaluation and social networks, specically in health promotion contexts.
Anupreet is a member of the Center for Applied Network Analysis, led by Dr. omas
W. V a l e n t e .
Amanda M. Beacom is a doctoral candidate at the Annenberg School for Communica-
tion, University of Southern California, USA. She conducts research on organizational
communication and social networks, particularly in health contexts.
omas W. Valente is professor in the Department of Preventive Medicine, Univer-
sity of Southern California, USA. He is the author of the books Social Networks and
Health: Models, Methods, and Applications (2010) and Network Models of the Diusion
of Innovation (1995).