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Network Theory and Intergroup Approaches

Summary and Keywords

A social network consists of interactive patterns among individuals and groups that are created by transmitting and exchanging messages through time and space. A central feature of intergroup settings is that group members are embedded in multiple, previously established, as well as emerging, communication networks that vary in their structure, the nature of the relationships, and the diversity of the links.

A network perspective extends and complements traditional social scientific approaches to intergroup communication. Rather than focusing on the attributes of individuals, a network perspective focuses on the causes and consequences of relations and connections between and among sets of people and groups. A network approach invigorates intergroup theory by focusing on the dynamic structures of connectedness, treating identity, social categorization, and representativeness as fluid rather than fixed factors within interactions.

A basic principle of network theory is that behavior can best be understood socially; every social unit stands at the nexus of a multitude of constraining and enabling alignments. Structural network dynamics include, but are not limited to, density, diversity, clustering, equivalence, and centrality of the network. These structural configurations combined with the strength and multiplexity of specific network linkages strongly influence social identities, values, attitudes, experiences, and behavior. Using graph-theoretic models, network analysts are able to identify specific types of structures that are highly effective in predicting ingroup and intergroup attitudes and behaviors above and beyond individual-level characteristics. Structural dynamics can further amplify intergroup principles through exploring the degree to which ingroup boundaries are loosely or tightly connected and the types and nature of linkages and communication exchanges within and between groups. For example, network theory suggests that the greater ingroup overlap across social contexts, the more likely group members perceive higher status for that particular ingroup than for other social categories to which they belong. It is also more likely the boundary between groups will be linguistically marked. In organizations, intergroup conflict and the capacity for successful adaptation and intergroup cooperation are strongly related to the extent and the alignment of intergroup “weak” ties across traditional communication channels and online. Identifying network structures can help explain a large set of multilevel intergroup outcomes such as linguistic accommodation and stereotyping, group level conflict, organizational productivity and innovation, political attitudes, and community resilience.

Keywords: social networks, structural dynamics, network configuration, cliques, multiplexity, network density, strength of weak ties, social capital


Intergroup communication involves multiple actors of distinct social categories who are part of a mutual feedback system in which social identity strongly influences communicative action. A central feature of intergroup settings is that members of all groups are embedded in multiple, previously established, as well as emerging communication networks that vary in their structure, the nature of the relationships, and the diversity of the links (Stohl, Giles, & Maass, 2016). Complementing these ideas, a network approach has, at its core, three central propositions: (1) every social unit stands at the nexus of a multitude of constraining and enabling alignments, (2) members of a group interact more often than a random set of people similar in number, and (3) the structural network dynamics of these connections strongly influence identities, values, attitudes, ideas, perceptions, experiences, and behavior. Social network theory spans a broad range of disciplines and embraces multiple perspectives, ranging from theories of self-interest, social identity, uncertainty reduction, intergroup relations, social exchange, and resource dependency to homophily and collective action (Monge & Contractor, 2003).

A social network consists of “patterns of contact between communication partners that are created by transmitting and exchanging messages through time and space” (Monge & Contractor, 2001, p. 440). A node represents the individual person or group in a network, and the link represents the type and strength of the communicative relationship. Social identity emerges through network interactions and these emergent network relations produce social capital, the aggregate resources that arise from organizational and personal connections and memberships in groups (Bourdieu, 1986). Like intergroup interactions, social networks span boundaries and can be analyzed at multiple levels of the social system, from egocentric networks in which all the links are either directly or indirectly connected to a focal node to networks that are composed of links created by and with small groups, organizations, communities, and national and global entities. For example, an organizational network may describe the interpersonal or intergroup relations within and across organizational boundaries whereas a team network may focus only on the connections among those directly related to a particular team.

Network connections may or may not be reciprocated, and the intergroup effects of both direct and indirect connections are dependent upon the overall structure of the network. Members of minority groups, for example, have differing advancement potential depending upon their specific network configurations. Ibarra (1995) found that it is not just that minority employees with high potential had more contacts outside their ingroups but that their networks were structurally balanced between same- and cross-race contact, had fewer high-status ties, and less overlap between their social and instrumental circles.

Relational Aspects of Social Networks

The basic unit of analysis for network research is a link between two nodes. The nature of the relational link can be described in many ways. Some of the most important relational constructs for understanding intergroup dynamics are (1) multiplexity, the degree to which nodes share multiple relations, that is, your cousin is also a member of your work group; (2) the strength of the link, that is, strong ties tend to be frequent, emotionally attached, and reciprocal and weak ties are neither relationally intense nor closely linked with many other ties in the network; and (3) the direction of the link, that is, horizontal ties include relations with friends, neighbors, relatives, and workmates, whereas vertical ties represent relations that transcend various social divides and hierarchical positions at both personal and institutional levels.

Network analyses have consistently shown that when network links are multiplex and have overlapping group memberships they are more likely to provide instrumental and social support as well as share normative expectations regarding linguistic and social behavior. In intergroup settings, a network perspective leads to the expectation that the greater ingroup overlap across contexts, the more likely group members perceive higher status for that particular ingroup compared to the perceived status of any other social category to which they belong. It is also more likely the boundary between groups will be marked by distinct linguistic choices (Giles, 2012). Strong ties also tend to foster group identity and the salience of particular social categories, and enhance cohesion and group solidarity. At the same time, strong ties are less likely to foster a climate of change and acceptance of difference within the network. Information sharing through strong ties tends to provide redundant information, thus limiting cognitive and communicative flexibility. Weak ties, on the other hand, typically provide diverse perspectives, bringing new and unique information into the network. When organizations develop strong linkages with other organizations and individuals within and across sectors (weak ties) they are typically more effective in meeting the complex challenges of a rapidly evolving and intergroup environment. However, when organizations within a sector are very tightly linked to one another, creating a highly interconnected dense network, organizational options are often constrained, and innovation, responsiveness, and learning is limited (Joshi, 2006). The connection between weak ties, information richness, openness to new ideas, and less rigid ingroup role behavior accounts, in part, for what Granovetter (1983) describes as “the strength of weak ties.”

Clearly, each of these relational network constructs is related to intergroup relations, but the real power of a network perspective lies in the ability to visualize and consider relational attributes within a larger network structure. In a classic social network study of London families, for example, Bott (1957) illustrated the importance of looking at the structural configuration of multiple network relational variables. She found that when each spouse has strong ties to individuals who are prototypical of their social category (e.g., husband/male, wife/female), setting up the expectation of strong adherence to normative sex role expectations in the marriage, conformity to these ingroup norms was moderated by the degree to which husband’s and wife’s networks overlapped and whether the network surrounding the family was densely or loosely connected. In other words, intergroup behavior and compliance with ingroup norms are mediated by network structures. At a more macro level, research suggests that intergroup conflict and the capacity for successful adaptation and intergroup cooperation are strongly associated with the extent and the alignment of intergroup “weak” ties across traditional communication channels and online (Wellman, Haase, Witte, & Hampton, 2001). Management scholars have also noted the close relationship between intergroup network diversity, organizational innovation, productivity, and interorganizational cooperation (Brass, Galaskiewicz, Greve, & Tsai, 2004).

Structural features of networks represent the configuration of the entire network, giving us an overall picture of the nodes in relation to one another. Density, one of most commonly used network measures, references the degree to which links are connected to one another, that is, the proportion of nodes in the network with direct ties to each other. Density is associated with the emergence of common norms, high levels of norm enforcement, ingroup solidarity, social and instrumental support, and outgroup distrust. These homogeneous, highly interconnected strong ingroup ties and cliques create bonding social capital, a sense of community identity and common purpose, a context of “thick trust” where trust extends only to ingroup friends and associates; outgroup members are regarded suspiciously (Coleman, 1990). In contrast, bridging social capital, characterized by less density and weak ties that transcend various social categories, is associated with the development of “thin trust,” whereby trust is extended to outgroup strangers (Putnam, 2000). Since the early days of network analysis, research has shown that under conditions of low density and network affiliations that cut across social groups there tends to be less ingroup loyalty and less likelihood of intergroup conflict (Guetzkow, 1955). However, for many years there has been an ongoing debate addressing whether network closure (social capital is created by a network of strongly interconnected elements) is more beneficial in intergroup settings than bridging social capital (where social capital is created by a network in which people can broker connections between otherwise disconnected segments [Burt, 2001]).

Relatedly, level of diversity references the degree to which a network is composed of similar links in terms of specific attributes including demographics, geographic location, and social categorization. When network nodes are diverse they tend to expand the boundaries of the group (that is, the nodes in the network are connected to a diverse set of links outside the network) and hence bring more and different types of information in to the network, providing greater opportunity for divergence, innovation, and intergroup cooperation.

Centrality measures the extent to which a particular node is connected to others in the network. There are three types of centrality (Freeman, 1978). Degree refers to prominence, that is, the extent to which a node is directly connected to others in the network. Closeness relates to access within and across the network and is calculated by assessing the minimum number of steps it would take for a node to reach (directly or indirectly) all other nodes in the network. Betweenness addresses the degree to which a node is directly connected to other nodes that are not directly connected to one another. Nodes that lie between other nodes are able to control resource flows within a network. These nodes are often called brokers; they bridge structural holes, which are the disconnected segments in a network. Centrality measures, therefore, provide a quantifiable index of a group’s communication (both intragroup and intergroup) and the relative resources, access, power, status, and influence available to members of different social groups. In studies of online communities, such as breast cancer forums, the most central ingroup members are more likely to adhere to, reinforce, and socialize outgroup members to the linguistic norms of the ingroup, and the less likely central members will accommodate to outgroups’ communication (Cooke, Shim, Srinivas, & Wu, 2015). Brokers also tend to have a competitive advantage over others in and outside their ingroups. They are also more likely to be promoted and receive other benefits associated with having more information and greater ability to organize and control the actions of other groups (Burt, 2001). From an intergroup perspective, brokerage also increases the likelihood and efficacy of the so-called accommodative chase, and the strategic encouragement to acquire the communicative practices of others (Abeyta & Giles, 2016).

Structural equivalence is another network construct that has important consequences for understanding intergroup dynamics. In network terms, two actors are structurally equivalent because they exhibit similar patterns of communication and occupy similar positions in their respective networks. They are not, however, necessarily connected to the same people (Hanneman & Riddle, 2005). When two groups or individuals occupy similar positions in society, they tend to develop similar behaviors and attitudes, possibly easing the way for communication while at the same time increasing the likelihood of competition.

The Emerging Influence of Network Analyses in Intergroup Research

Despite strong connections between social networks constructs, social identity, and intergroup behavior, the direct focus on networks in intergroup studies, outside of managerial studies, is relatively recent and somewhat limited. The emergence of digital technology, however, has highlighted the relevance of social networks. Specific network configurations on Twitter, Facebook, and other digital platforms, for example, have been shown to promote ingroup cohesiveness, intergroup debate (see Walther & Carr, 2010), and even multilingual development (Androutsopoulos, 2015), but dense and homophilous online networks help facilitate and promote the health of stigmatized groups (Rintamaki & Brashers, 2010). However, issues of receptivity to fake news, bubble filters, and political polarization all point to potential dangers of homogenous social networks. The ease with which platforms like Twitter and Facebook enable finding like-minded, structurally equivalent others who will reinforce ingroup beliefs regardless of the existence of contrary information is of great concern to scholars and civil society (Garrett, 2016).

Intergroup conflict and contact studies are also now being infused with a network perspective as several meta-analyses illustrate the value of a thinking about the networks in which individuals and groups are embedded. For example, a large-scale review by Davies, Tropp, Aron, Pettigrew, and Wright (2011) provides strong evidence that cross-group friendship networks are especially powerful forms of intergroup contact especially in terms of approach and avoidance after intergroup conflict, the mediation of intergroup conflict, and the severity of the conflict. A meta-analysis of 515 intergroup contact studies across national samples also found indirect network effects in intergroup interactions. Ingroup friends who have friends who have outgroup friends are less prejudiced, experience greater intergroup trust, and are more willing to forgive past between-group transgressions (Pettigrew, Tropp, Wagner, & Christ, 2011). In a longitudinal network analysis of adolescent children, indirect network effects also helped explain why prejudiced majority group members formed fewer intergroup friendships than less prejudiced majority group members. Preteen preference to become “friends of one’s friends’ friends” and the tendency to avoid friends who already had minority outgroup members in their network limited the potential positive intergroup contact (Stark, 2015). Using social identity theory as the foundation of their study, Boda and Néray (2015) also looked at ingroup and outgroup friendship networks of Roma and non-Roma Hungarian high school students. They found that minority students excluded from their social network those whom they perceive as minorities, but who, at the same time, identify with the majority group. These same types of network dynamics are found in studies of intergroup contact and conflict in the workplace (e.g., Hargie, Dickson, & Nelson, 2003).

Even within these studies, however, there is limited use of network theory and the wide ranging and theoretically rich and relevant network constructs that are often found in interpersonal, organizational, and community research (Parks, 2007; Monge & Contractor, 2003). Moreover, few intergroup studies utilize the recently developed and easily accessible network analytics and visualization techniques (Borgatti, Everett, & Freeman, 2002). As a result, calls for greater integration of network and intergroup approaches are common. In 2015 Vezzali, Hewstone, Capozza, Giovannini, and Wűlfer acknowledged that social network analyses is critical for advancing the accuracy of assessment and understanding of intergroup relations but lamented the limited nature of network studies of intergroup relations. In a review of the intergroup literature Stohl, Giles, and Maass (2016) also explicate the under-studied and under-theorized roles of networks in understanding intergroup communication and develop intergroup network principles and propose several connections between network constructs and intergroup dynamics. For example, they suggest that the greater ingroup density, fewer bridges, more structural holes, and fewer weak ties of a network, the more likely individuals will “non-accommodate” and accentuate their ingroup code in intergroup encounters. Lazega and Snijders (2016) have deftly combined network analysis with multilevel analysis that helps further the development of organizational and intergroup theory building.

Analyzing Networks in Intergroup Settings

A fundamental feature of social network research is that individuals and groups are defined relationally rather than categorically. These connections may or may not be reciprocated, and both the direct and indirect connections are dependent on others’ behavior. Because social network analysis focuses on the causes and consequences of relations between people and among sets of people rather than focusing on the attributes of individuals, as typically done in social science, there are unique opportunities as well as methodological challenges. Social network analysts use two kinds of tools from mathematics to represent information about the patterns of ties among social actors: graph theory and matrix algebra (see Hanneman & Riddle, 2005).

By quantifying relationships between and among people, network approaches have several unique properties. Network methods cannot be based on the conventional assumptions of independence and autonomous units of analysis. There are inherent interdependencies, reciprocities, and mutual influence among network links that required the development of several new statistical procedures and measures (see Wasserman & Faust, 1994). But not only do these specialized graph-theoretic methods enable researchers to identify specific types of structures that are highly effective in predicting attitudes and behaviors above and beyond individual-level characteristics, advances in agent-based modeling and computer simulations enable researchers to identify how networks evolve over time. These techniques provide powerful mechanisms for theoretically modeling the evolution of cooperation, conflict, and other intergroup dynamics (Monge & Contractor, 2003).

Moreover, because the focus in network analysis is on connectivity among individual actors or groups, actors cannot be sampled randomly. The data need to include all (or at least most) of the other actors with whom the focal individual or group has (or could have) ties. As a result, network approaches tend to study whole populations by means of census, rather than by sample. Common data collection methods include giving every member of a group a roster of all other members in a specified environment (e.g., a family, an organization, a community) and respondents are asked (1) to identify the people with whom they interact and (2) to specify the nature of the relationships. These self-reported data are then used to develop a sociogram, that is, a structural configuration of all the connections in the network. Network data can also be collected from archival sources such as attendance records of meetings, diaries, telephone logs, email chains, newspaper reports, social network platforms such as Facebook and Twitter, and observational techniques. New and creative means of collecting network data are continually emerging. The necessary feature of all network data collection methods is that the data represent connections, not individual attributes.

An intergroup network approach also requires research designs in which the experiences of network members with outgroup members beyond the participants’ direct experience must be considered. Indirect connections and vicarious exposure to outgroup members through friends and friends of friends, for example, have been shown to influence an individual’s intergroup anxiety, attitudes, and willingness to interact with outgroup members (Vezzali, Hewstone, Capoza, Giovannini, & Wűlfer, 2015). In order to study both the direct and indirect facilitators of intergroup communication, it is critical to specify boundaries that transcend direct interaction and assess the number and quality of outgroup contacts that other members of each individual’s network have.


Overall a network approach to intergroup behavior provides the opportunity to study “connectedness in action” (Stohl, 1995). It allows researchers to move beyond individual-level attributes and identify group structures (such as clusters or cliques), which are predictive of future intergroup attitudes and behaviors. Moreover, as recent work in management and organizational studies illustrates, the challenges of understanding intergroup dynamics can be more fully addressed by paying closer attention to multilevel network phenomena.

The connective logic of social network theory can also be leveraged for intervention programs aimed at improving intergroup communication. Computer simulations as well as experimentally created groups enable researchers to explore both long- and short-term effects of network configurations on future network structures and intergroup attitudes. Vezzali, Stathi, Giovannini, Capozza, and Visintin (2015), for example, experimentally manipulated children’s intergroup networks and within three months the likelihood of the children’s actual networks including outgroup members increased. Integrating a network perspective into intergroup dynamics will not only continue to enrich and elaborate central propositions of intergroup theory but will also create theoretical and practical insights.


Although the 21st century has been heralded as the “age of networks,” representing a paradigmatic shift in the way we think about how people, objects, and events are linked and what these connections mean for understanding the ways we perceive and interact in the world, the powerful relationship between network dynamics, social connectedness, and intergroup dynamics is not new.

In the late 1800s, sociologist Émile Durkheim (1951) argued that social phenomena cannot be accounted for in terms of attributes or traits of individuals, but rather, need to be understood in the context of the dynamic structure of relations. He was the first European social scientist to argue, for example, that the causes of suicide were to be found in social collective factors not only in individual qualities. “Egoistic suicide,” Durkheim posited, was the result of detachment from society, where people were not integrated into society by ties to family and community, work roles, and other social bonds. At the same time, Ferdinand Tönnies (1887) made a highly influential distinction between gemeinschaft and gesellschaft. The difference was based upon the degree to which communities’ social connections were characterized by reciprocal bonds of affect and kinship or were impersonal and functionally developed. These differences, he contended, had important consequences for the ways in which ideas like status, deviance, independence, and identity are defined and actions like support and influence are developed.

In the same period Georg Simmel pioneered ideas on social networks, highlighting the consequential differences between dyads and triads and the influences of group size and the likelihood of interaction and conflict in loosely knit or tightly knit networks (Simmel, 1922). In his classic work Conflict and the Web of Group Affiliations Simmel noted how conflict with one group may serve to produce cohesion such that in a society whose social bonds are disintegrating, intergroup conflict may restore the integrative core. In anthropology, Radcliff Brown’s groundbreaking analyses of non-Western cultures (1922) incorporated ideas of structural functionalism focusing on the relevance of kinship bonds and the socio-structural organization of societies for the evolution of culture. In linguistics, the pioneering work of de Saussure (1916) was infused with notions of connectedness and was an early proponent of what is now known as semantic network analysis. He argued that meaning of signs can only be understood by examining networks of relationships among signs and the system (or structure) in which the sign is embedded.

These early theoretical ideas on connectedness were followed by influential empirical as well as theoretical development by behavioral scientists such as Moreno (1930s), Bavelas (1950s), Milgram (1960s), Granovetter (1970s), Rogers (1980s) Burt, (1990s), and Christakis and Fowler (2000s), among many others. Identifying relevant network constructs such as intragroup density and intergroup centrality, network brokering and structural equivalence, and linking contemporary societal trends of globalization, international management, and digital communication to intergroup network studies embody an important frontier in understanding the nature of ingroup and intergroup communication.

Primary Sources

The most comprehensive textbook for an introduction to network analysis is the 2005 text by Hanneman and Riddle, Introduction to Social Network Methods. Scott’s 2012 Social Network Analysis also provides an excellent commentary on network analysis in the social sciences. Tom Valente’s (2010) text Social Networks and Health: Models, Methods, and Applications is very useful for those interested in intergroup dynamics and network health disparities. Two important journals exclusively dedicated to empirical, theoretical, and methodological studies and issues utilizing social network analysis are Connections published by the International Network for Social Network Analysis and Social Networks: An International Journal of Structural Analysis.

There are also several academic centers dedicated to social network studies that provide informative websites for finding out about advances in social network theories, methods, and tools. Many of these sites include access to classic and large-scale network data sets. Some of the best known centers are SONIC, Science of Networks in Communities, CASOS, the Center for Computational Analysis of Social and Organizational Systems, the Stanford network group, and the Duke Network Analysis Center. Network analysis software packages are widely available. Many of the most popular analytic programs have been developed by Analytic Technologies, which includes UCINET for network data analyses and the network visualization programs Netdraw and Krackplot. Longitudinal network analytic programs such as SIENA and Sonia enable researchers to explore network development and effects over time. These programs make cross-sectional and longitudinal network data sets easily accessible.

Further Reading

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                            Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. (2004). Taking stock of networks and organizations: A multilevel perspective. Academy of Management Journal, 47, 795–817.Find this resource:

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                                                    Hargie, O., Dickson, D., & Nelson, S. (2003). Working together in a divided society: A study of intergroup communication in the Northern Ireland workplace. Journal of Business and Technical Communication, 17(3), 285–318.Find this resource:

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                                                          Lazega, E., & Snijders, T. (Eds.). (2016). Multilevel network analysis for the social sciences theory, methods and applications. New York: Springer.Find this resource:

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