Blog 5: Centrality Measures in Adolescent Research

Node centrality measures serve as an additional way, beyond the visual of a graph, to understand social networks. These measures provide more detailed evidence about how the network itself, and the players within it (the nodes) function. For example, the eigenvector centrality metric gives us an idea of which nodes are most influential in the network, serving as an indicator for spotting important nodes. Additionally, the betweenness centrality metric is basically the idea of how regularly a given node serves as a bridge or intermediary between other nodes in the network. In this way, centrality measures provide information not just on the number of edges connected to each node (as can be seen in the visual graph like the one below), but also explains the role that each of the nodes play in the network, and how their role impact larger network functioning.

Ennett and colleagues (2006) provide an example of how centrality measures such as betweenness centrality and a few other centrality measures that we haven’t specifically discussed in class. In their study of substance use in adolescent peer groups, they describe betweenness centrality as “the extent to which an adolescent in-directly links pairs of adolescents who are not directly linked as friends” (p. 168). They found that 45% of the adolescents in their sample served as bridges between neighborhood peers, and that betweenness centrality was significantly and positively correlated with the number of neighborhood substance users. However, other measures of centrality were more likely to be significantly associated with personal substance use, suggesting that the adolescents who are most visible in the network are more likely to use substances than the ones who serve as intermediaries. In this example, we see how the use of betweenness and other centrality measures allow for the understanding of how nodes in the network function, and the relationship between network positionality and an outcome of interest.

Similarly, Russell and Smith (2011) show the importance of using centrality measures such as degree centrality in their investigation of the afterschool network program in Dallas, Texas. For example, through exploring the degree centrality of certain programs, there were able to identify which programs were vulnerable due to the fact that they had just one or two sources of funding, and tended to work in isolation. The image below shows the differences across the network in relation to their funding structures. Programs in the clusters on the left generally receive funding from one or two organizations; whereas programs in the cluster on the right have more diverse funding mechanisms and are more integrated into the community of afterschool program providers.

Results from these two studies demonstrate the different ways in which measures of centrality can be used to understand not only network shape but also how nodes within a network differ in relation to each other, how edges in a network develop into clusters, and how these clusters and measures better allow us to understand our research questions.

 

References:

  • Ennett, S. T., Bauman, K. E., Hussong, A., Faris, R., Foshee, V. A., Cai, L., & DuRant, R. H. (2006). The peer context of adolescent substance use: Findings from social network analysis. Journal of research on adolescence16(2), 159-186.
  • Russell, M. G., & Smith, M. A. (2011). Networks Analysis of a Regional Ecosystem of Afterschool Programs. Afterschool Matters13, 1-11.

 

 

Social Capital, Bowling Alone, and #MeToo: Blog 4

Are we really bowling alone? Are we really pulling away from communal living in light of technological increases over the past few decades? In many ways, I think a superficial answer to this question is yes… Putman (1995) discusses the overall decreases we have seen in Americans participation in communal activities such as volunteering, religion, and bowling. Given his claim that participation in communal activities leads to the development of social trust and reciprocity, it makes sense that Americans disengagement with such activities would lead to a devaluing of social capital. However, social movements such as #MeToo and #BlackLivesMatter, which largely grew from technological platforms lend some evidence to the idea that Americans are still actively engaging in communal activities even though these activities are no longer bowling leagues.

The #MeToo movement serves as a prime example of how social capital interacts in networks. Lin (1999) discusses how social capital facilitates the flow of information, influences decision makers, and demonstrates ones’ social credentials. In the communal act of the #MeToo movement we see how engagement in this simple activity flowed information rather quickly through a gigantic online social network. The image below shows the spread of this movement in just 12 hours.

Additionally, this movement was the topic of discussion in policy circles and served to identify social credentials of the silence breakers (pictured on the cover of time magazine).   Thus, this movement serves as evidence that the emergence of the Internet has not devalued social capital, but rather altered the way we engage with social capital and in social networks,

For one, Lin (1999) sates that the Internet has broadened social networks, providing more access to free information and social capital than before. In this way, social capital isn’t as tightly controlled by the upper class and decision makers. In many ways, this allows for the emergence of new voices in the policy arena. However, it is important to note that social capital is not always a net positive for actors. Portes (2002) exemplifies this idea with the discussion of how social capital excludes outsiders and demands conformity. Using gangs as a prime negative example of social capital, he discusses how conformity can promote violence, ostracize new actors from engaging, and even place individuality at too high a cost to be achieved within a community. This results in stifled innovation, progress, and productivity.

In thinking about the value of social capital in research, I am drawn to the idea of community social capital. For example, given the newer emergence of an equity lens in educational research, it is important to consider which individuals hold more social capital and how that relates to educational experiences and outcomes. For example, are students with more social capital afforded better educational experiences and outcomes than their peers? Can SNA identify instances where this is happening and develop intervention to abate it? For my personal research, I am interested in considering who holds social capital both within afterschool program networks, and within the programs themselves. What resources do each of the actors in the network bring and how does this relate to the broader community social capital of the neighborhood within which the program is situated?

Leaving you with a question:
Kaushadin (2011) brings up the idea of social solidarity as a facet of social capital. This is basically a shared trust and reciprocity within a community. My most poignant example of this was experienced when I was a student at UVA. Given our strict honor code and a communal respect and adherence to it, it was very common for students to leave their valuables unattended for hours at end, trusting our community to abide by the code. Can you think of an example of such explicit social solidarity that you have experienced within a community?

It’s a Small World After All…

“What is the probability that any two people, selected arbitrarily from a large population, such as that of the United States, will know each other?”

This is the question that Travers and Milgram (1969) sought to investigate, and in essence defines the small world theory. The Small World Theory basically is the idea that our unexpected social links connect us to far more individuals than we realize, regardless of location. Kadushin (2011) demonstrates this idea in the first chapter of Understanding Social Networks by discussing how social media sites such as Facebook connect us to networks that make the world seem small. For example, through my 500 friends on Facebook, I am connected to various other individuals by either one or two degrees of separation. In other words, I am connected to my friends, their friends, and further their friends. The expansive nature of this system can be visualized in the image below.

However, these connections don’t solely exist online. This week’s lectures also demonstrated the small world theory, as it exists practically in our policy arena. The lecture mentioned Obama’s decision to change telephone surveillance of terrorists from 3 degrees to only 2 degrees of separation, demonstrating how wide our social networks go and the actual number of individuals who are still under surveillance given the change.

However, this example also highlights one of the very real risks of the small world theory. Kadushin (2011) warns that these weak ties have the danger of exposing us to more people than we desire to be exposed to. This seems to occur for a few reasons:

  1. Kadushin (2011) explains that weak ties are what connect networks that otherwise would be disconnected, connecting us outside of our insular networks.
  2. Granovetter (1973) claims that “the stronger the tie connecting two individuals, the more similar they are” (p.1362), which seems to suggest that weak ties connect us to those whom we are dissimilar from.

For these reasons, weak ties, such as those emphasized in the small world theory connect us to those who we are different from, and otherwise would likely not be connected to. This provides the opportunity for us to 1) be connected to more people than we desire and 2) be connected to individuals we may wish we weren’t connected to.

So, what is the impact of weak ties on the big world?

As previously mentioned, weak ties connect us people we otherwise would not be connected to. Further, Granovetter (1973) explains how weak ties allow us to explore connections between social networks, stating:

“Emphasis on weak ties lends itself to discussion of relations between groups and to analysis of segments of social structure not easily defined in terms of primary groups” (p. 1360).

Thus, the presence of weak ties allows us to consider the social networks of the world as cohesive as opposed to disjointed systems. We therefore can investigate the connection between various networks instead of solely focusing our research within insular networks. Due to this, we can begin to collect data and ask questions about how networks are connected, how we maintain such connections, and the importance of these bridges.

From this perspective, weak ties seem to be just as important as strong ties. In my opinion, they may even rival strong ties. Given that they are the source of newer ideas being introduced into social groups from dissimilar groups, they serve an important role of ensuring that our social networks do not become too isolated. This perhaps is most evident when considering the impact that weak ties likely played in the last presidential election.

So, who is Kevin Bacon, and why is he important in this discussion?

Kevin Bacon, the actor in the photo above, became the face of the small world theory when a few college students designed a game called “The Six Degrees of Kevin Bacon.” The goal of this game is to connect any actor to Kevin Bacon through no more than 6 other actors, thus demonstrating that even the world of Hollywood is subject to the small world theory.

Understanding our Modern Society

Given what we learned last week about the power of weak and strong ties on individuals, it is clear that social networks have the power to influence individuals’ behaviors, emotions, outcomes, etc. In this way, social networks provide us with the opportunity to explore human tendencies, and further understand how human beings exist in and engage with modern society. The social network perspective, according to Keim (2011) is “explains social actions not on the basis of individual attributes but in the context of social relations (Wellman, 1988: 31).” (p. 21). That being said, social network analysis steps beyond understanding modern society as a product of individuals’ traits or circumstances, and considers how the interactions between individuals or the interactions between systems influence such actions.

If however, we are considering interactions between individuals, it is important to consider the perspective from which we are viewing such networks. For example, in egocentric studies only the “network immediately surrounding participants is observed” (Robins, 2015, p.40). This means that our perception of a network is limited to what is immediately surrounding participants, and lacks information about how the network expands beyond these people or how it might be experienced by others. Thus, if we were to shift to the perspectives of a different sample of participants, the network likely would look different. Given this, it is important to consider the bias in SNA just as we would in any other traditional social science design.

So then, how does SNA data differ from traditional social science data?   SNA data differs from traditional social science data in that it necessitates two data sets, namely the nodes and the relations between them. For example, on our practicum, we had one data sheet with the characteristics / attribute of the nodes– similar to what we would see with traditional social science data (sheet A). However, this data sheet was paired with a data set that recorded the connections or relations between the nodes (sheet B). Thus, one of the main differences in SNA and traditional social science is the format of the data itself. Further, the main unit of analysis in SNA is the node or the tie as opposed to an attribute or outcome in traditional data analysis.  In this way, relational data must be treated differently than our average data set.

Though inherently different, relational data still necessitates descriptive analysis so that similarities and differences between nodes and ties can be considered. Through these descriptions researchers are able to consider the similarities and differences in groups within the network, further explaining how humans engage similarly and differently with modern society. However, diverging from traditional data analyses, one key reason that relational data makes predictive analysis challenging is because of causal ambiguity. Yang, Keller, and Zheng (2017) define causal ambiguity as the fact that we don’t know which data point has caused change in the other. Instead, we know that the two are related non some factor.

Overall, there seem to be some similarities between SNA and traditional social science research. However, SNA provides us with a new language and new techniques to further explore human beings’ interactions, emotions, and daily actions in modern society.

A thought-provoking quote:

  • “From a network perspective, one would argue that peers are not influential per se, but can become important if they are engaged in frequent contact, build cohesive networks of high density, transmit information quickly, and produce homogenous evaluations and normative pressures” (Keim, 2011, p. 21).

 

Blog Post 1: Connected

Never before had I considered the drastic impact that networks have on our lives. As a rather well connected young adult, I have thought extensively about relationships. However, these thoughts have often stopped at the individual relationship level, and never extended to my social networks. In their book Connected, Christakis and Fowler dive deeply into explaining the power of social networks’ influence on our daily lives. Beginning with social networks broadly, they distinguish between the ideas of connection and influence. Connection, which goes with the idea of “six degrees of separation”, indicates that six steps connect us to another person. However, they discuss the idea of three degrees of influence, suggesting that actual influence can travel three separate degrees. Therefore, we affect our friend, our friend’s friend, and our friend’s friend’s friend.

Figure 1. Three degrees of influence

 

But how do we affect these people? Christakis and Fowler discuss a few specific ways in which our social networks affect us:

  1. They affect our outcomes. Citing the “rich get richer” phenomenon, Christakis and Fowler discuss how social networks tend to reinforce both situational and positional inequality.
  2. They affect our emotions. Referencing examples of mass psychogenic illnesses such as this video below, they discuss how emotions are transmitted like diseases through social networks, infecting those in our sphere of influence. In this way, they discuss how emotions such as happiness and loneliness spread through social networks.

  3. They affect our knowledge. Citing examples of suicide contagions, social activism, and successful presidential campaigns, Christakis and Fowler demonstrate how the spread of information through social networks affects our knowledge basis and as a result impacts our actions such as voting or engaging in activism activities.

Focusing specifically on digital networks and the Internet, Christakis and Fowler address both the pessimistic and optimistic views of how the Internet impacts our social networks. On one hand, people feel that social media hinders face-to-face interaction and communication skills. However, others feel that digital networks and the Internet only extend our opportunities for social interaction and networks. I am inclined to side in the middle. I think that the Internet does provide opportunities to extend our social networks in healthy ways by providing access to wider communities and opportunities to practice social skills. However, I do think we run the risk of being consumed by virtual interactions as opposed to face-to-face interactions, and therefore, like all things, the Internet should be used in moderation for these purposes. Life is about balance.

However, the authors note that regardless of opinion, social networking sites shape social networks in four distinct ways.

  1. Enormity – they increase the scale and reach of our social networks.
  2. Communality – they extend the ways in which we share information and collaborate
  3. Specificity – they further particularize the ties we can make within networks
  4. Virtuality – they allow us to assume separate virtual identities

These are four impacts that I can agree with — and note that I have seen evidence of in my life. Generally, I think that Christakis and Fowler do a good job of explaining and evidencing the ways in which the Internet impacts our social networks, which consequentially impacts our actions, beliefs, and behaviors. At large this book has encouraged me to consider the ways in which I personally am influenced by my social networks. On a more academic level, it has prompted thoughts on how the students I work with are influenced by their social networks, and how these networks impact their decisions to engage in productive afterschool activities.

Two Quotes that struck me from this book:

  1. “Happiness is thus not merely a function of individual experience or choice; it is also a property of groups of people” (p. 66).
  2. “Loneliness can actually shape the social network. People who feel lonely all the time will lose about 8 percent of their friends, on average, over two to four years. Lonely people tend to attract fewer friends, but they also tend to name fewer people as friends as well. What this means is that loneliness is both a cause and a consequence…” (p. 70).