My social network analysis

The structure and nature of networks is a fascinating topic indeed, and the quantitative nature of digital data makes analysis of online human networks not only relatively easy, but pretty insightful. I took Howard Rheingold’s cue in Net Smart to search for “visualize Facebook social network” (pg 203) and applied it my personal Facebook friends. What I learned revealed some statistics I hadn’t considered before, but more interesting, intriguing insights into how friends with certain social network strengths make up my top connections.

After a Google search for the phrase above and a look around at the results, I chose Wolfram Alpha’s Personal Analytics for Facebook. I had to register an account through my Facebook page, but I haven’t noticed any unwelcome posts, and the analysis was totally free and quite thorough. I recommend trying it out for yourself! Wolfram Alpha looks at post, like, comment statistics for statuses, photos and links, word frequency analysis on status posts and a lot of personal data in addition to the network analysis, the topic of this post.

Mutual Group ClustersMutual Group Clusters

The first complex network analysis was groupings of friends according to “mutual group clusters”, which, not surprisingly, seemed to group friends into collective experiences like family, work, and school.

The largest group (medium blue cluster on the right) seemed to consist my closest friends and friends of those friends, mostly people I considered in my “real life” social peer network. I invite these people to parties, go to theirs, we visit the same bars, know the same people.

The next largest group, (darkest blue on left) consisted entirely of people I went to high school with. This is a great example of a network that, to me, is strictly online and almost painfully superficial. I have exactly two high school friends I still socialize with on a regular basis, so most of this group is people I haven’t seen face-to-face for almost 15 years. For many, it is unlikely that I even interacted with them much during high school!

The next group (slightly darker blue in the middle-right) is family, including some of my parent’s closest friends. Smaller groups include coworkers I had at a newspaper group (blue-green), my husband’s family (medium green), and schoolmates from my undergrad studies (green-yellow).

To me, the most interesting set of friends were the outliers (at the bottom in orange and red), who each were assigned their own “group”. These friends had no connections to my larger network except through me, we provide for each other unique connections to otherwise unconnected networks.

color coded friend networkNetworked Roles

The second and most insightful network analysis assigned social roles to certain friends. These roles described highly connected friends in terms of their relationship to me and my network as well as their connections to other networks. This analysis gave me an important insight into which of my Facebook friends have influence and access inside or outside of my network.

Wolfram Alpha defined five different social network roles and assigned “top” friends to each who exemplified the defined role.

The first role is “social insider”, represented on the graph in purple. According to WA, “a social insider is a friend who share a large number of friends with you. Social insiders typically appear in the center area of your friend network.” My “top social insiders” include my husband, brother, mom, and oldest friends and would be important people for influencing my established network.

The next role, “social outsider”, is represented in gray. Like the outliers in the group cluster graph, “a social outsider is a friend who share at most one friend with you.” (WA) These friends could offer access to entirely new, foreign networks. My neighbor is a good example of one of my social outliers.

In green are my “social connectors”. This is a friend “who connects together groups of your friends that are otherwise disconnected.” This is one of the most important roles, acting as a hub to connect disparate social groups and affecting influence on several groups within a network. This role includes close friends and family, similar to my social insiders.

Next, in orange, are “social neighbors”, those friends “with a small number of out-of-network friends (friends of theirs that you don’t know).” These people are more integrated into my shared network than they are integrated into other networks, so they have a strong vested interest in the same networks and friends. This includes many of my older family members who haven’t established extended Facebook networks.

The last role is that of “social gateway”, “someone with a large number of out-of-network friends.” My top social gateways are my connection with very large number of friends. I don’t consider myself close to most of them as personal friends, but their potential to reach others can’t be underestimated. My top social gateways include my very outgoing younger cousin, a popular friend of my mom’s, and a classmate from undergrad who is in a band.

This analysis has been so insightful and while it felt superficial and egocentric at first (sometimes what Facebook feels like in general), it caused me to think about outwardly about the varied people in my virtual network, their inherent value and humanity as individuals with unique networks. It is a useful tool for those who want to reach local and new networks effectively as well.

About Michelle Mailey Noben

I'm a graphic designer and graduate student at the University of Wisconsin–Stout in Menomonie, Wis. I'm in my second year of the School of Art + Design's Master of Fine Arts in Design program. So far, it's been a great experience, although challenging at times to come back to academia after working in the industry for several years. When I'm done with my studies, I'd like to teach at the adult level. I work for the University as Graduate Assistant in the University Library, where I work with the Public Relations committee on promoting library events. This year, I recently started in an office assistant position in the School of Art + Design's program office. I'm looking forward to becoming more comfortable with emerging media to make the most of this amazing technology. Thanks for reading!

Posted on October 26, 2014, in Uncategorized. Bookmark the permalink. 5 Comments.

  1. This is awesome! I haven’t used Facebook much in the past year, but it was really cool to see my WolframAlpha report. I took a picture of my friend network, but I don’t know how to add an image to your blog posting.

    Here is a link to my report. http://po.st/q65x5v

    • Michelle Mailey Noben

      Thanks for your response! I wasn’t able to view your individual report with the link, but maybe you have some thoughts about what the data showed for you?

      Michelle

  2. This is really interesting. While reading through your post, I was reminded of Rainie & Wellman’s reading from “Networked”. How do you think you will use the information that you’ve found? Was mapping out your network helpful in identifying connections for specific information?

    • Michelle Mailey Noben

      Thanks for your response!

      I really hesitate to think of the people in my network in terms of what they can do for me, but in that vein I think the biggest takeaway was insight into which of my connections are gateways to entirely different networks and how those people could, if obliging, disseminate my ideas and links to their networks.

      The report was also very helpful for me in visualizing just how interconnected, and maybe even insulated, my network is. I really only had about 5 connections who were part of my network solely through me. Everyone else was connected in another way, too. So my second big takeaway might be that I should make more efforts to connect with people outside of my existing network, to take advantage of alternate cultures and viewpoints, and gain access to new networks.

      Michelle

  3. Very cool! I’m going to share this with my digital humanities students tomorrow as we are starting a visualization unit. See “How to Compare One Million Images?” by Lev Manovich, with more of his current work available at http://lab.softwarestudies.com/.

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