Networks and Religion: What is Social Network Analysis? (Probably Not What You Think)

Nicholas Christakis begins his widely-viewed 2010 TED talk, The Hidden Influence of Social Networks (which can be viewed at the bottom of this post), with a story from when, in the 1990s at the University of Chicago, he was studying the “widower effect.” And he tells how he was caring for a woman who was dying from dementia and being cared for by her daughter. “And the daughter was exhausted from caring for her mother. And the daughter’s husband, he also was sick from his wife’s exhaustion. And I was driving home one day, and I get a phone call from the husband's friend, calling me because he was depressed about what was happening to his friend. So here I get this call from this random guy that’s having an experience that’s being influenced by people at some social distance." This led him to experience an epiphany:

I suddenly realized two very simple things: First, the widowhood effect was not restricted to husbands and wives. And second, it was not restricted to pairs of people. And I started to see the world in a whole new way, like pairs of people connected to each other. And then I realized that these individuals would be connected into foursomes with other pairs of people nearby. And then, in fact, these people were embedded in other sorts of relationships: marriage and spousal and friendship and other sorts of ties. And that, in fact, these connections were vast and that we were all embedded in this broad set of connections with each other. So, I started to see the world in a completely new way and I became obsessed with this. I became obsessed with how it might be that we’re embedded in these social networks, and how they affect our lives.

Christakis is not the first to become obsessed with social networks. Since the early 20th century, social scientists have explored the dynamics of the networks in which individuals are embedded. For instance, Georg Simmel argued that in order to understand social behavior we must study patterns of interaction, and he offered novel insights into the nature of secret societies and how increasing social complexity contributed to the rise of modern individualism. And beginning in the 1960s Harrison White, who also earned a Ph.D. in theoretical physics, argued that in spite of its claim to study social phenomena, sociology was beholden to individualistic forms of analysis based on the aggregated characteristics of individuals. This, he believed, was a mistake, and, along with his students, he developed an approach that drew on case studies that focused on social ties and the patterns that emerged from them. These efforts didn't occur in a vacuum but were instead informed by other theoretical traditions, such as graph theory, exchange theory, and research into the recruitment of individuals to religious and social movements.

To say that the discipline has come into its own would be an understatement. Social network analysts have created their own organization, launched their own journals, and produced a number of excellent monographs. In recent years, economists have become increasingly interested in social networks, as have physicists and other scientists.

What is social network analysis (SNA)? Briefly put, it is a collection of theories and methods that assumes that the behavior of actors (whether individuals, groups, or organizations) is profoundly affected by their ties to others and the networks in which they are embedded. Rather than viewing actors as unaffected by those around them, it assumes that interaction patterns affect what actors do, say, and believe. Although some interactions are random, many are not. Actors tend to interact with similar others and repeated interaction can lead to the emergence of social formation at multiple levels. SNA differs from more traditional approaches in that while the latter tends focus on actors’ attributes (e.g., gender, race, education), SNA focuses on how interaction patterns affect behavior. It notes that while attributes typically do not vary across social contexts, most interaction patterns do, suggesting that interaction patterns are just as (or perhaps more) important for understanding behavior:

A woman who holds a menial job requiring little initiative in an office may be a dynamic leader of a neighborhood association and an assertive PTA participant. Such behavioral differences are difficult to reconcile with unchanging gender, age and status attributes, but comprehensible on recognizing that people’s structural relations can vary markedly across social contexts (Knoke and Yang 2007:5).

Consequently, a primary goal of SNA has been to develop metrics and algorithms that help us gain a better understanding of a particular network’s structural features. It has been used successfully to explain a variety of behavior from Fortune 500 corporations (Mizruchi 1996) and Christian denominations (Chaves 1996), to social movements (Hanssanpour 2016) and terrorist organizations (Cunningham et al. 2016).

Misconceptions

Sometimes SNA is confused with social media (it doesn't help that the movie about Facebook was called, "The Social Network"), but while we can use SNA to analyze Facebook, Twitter, and the like, it isn't the same. SNA is a collection of theories and methods that have been developed to understand the structure of social networks, whereas social media is user-generated content that can include text, pictures, videos, connections among users, and links to websites. Analysts can extract network data from social media platforms and use SNA to understand those social media networks, but that is different. The fact that social media content is often relational and can therefore lend itself to SNA, only adds to the confusion.

How some use the term “network” can also be confusing. Some use it to refer to decentralized, informal and/or organic types of organizations. And while this distinction can be useful in some contexts, within the world of SNA, all organizations are seen as networks. Some may be more hierarchical than others, but they are still networks, which is why social network analysts have developed algorithms that measure the degree to which a particular network is hierarchical.

Networks and Religion

That social networks play a central role in religious life is fairly well established. We know, for instance, that they are crucial for the recruitment and retention of members, the diffusion of religious ideas and practices, motivating individuals to volunteer and become politically active, the health and well-being of people of faith, and conflict, radicalization, and (sometimes) violence. However, most of the research in this area has been conducted by social scientists unfamiliar with social network methods and thus use proxies for networks that are marginal, at best. Thus, a primary purpose of my new book is to facilitate the study of networks and religion using formal SNA methods.

References

Chaves, Mark. 1996. "Ordaining Women: The Diffusion of an Organizational Innovation." American Journal of Sociology 101(4):840-73.

Cunningham, Daniel, Sean F. Everton and Philip J. Murphy. 2016. Understanding Dark Networks: A Strategic Framework for the Use of Social Network Analysis. Lanham, MD: Rowman and Littlefield.

Hanssanpour, Navid. 2016. Leading from the Periphery and Network Collective Action. Cambridge, UK: Cambridge University Press.

Knoke, David and Song Yang. 2007. Social Network Analysis. Thousand Oaks, CA: Sage Publications, Inc.

Mizruchi, Mark S. 1996. "What Do Interlocks Do? An Analysis, Critique, and Assessment of Research on Interlocking Directorates." Annual Review of Sociology 22:271-98.

Video: The Hidden Influence of Social Networks