This is my homework assignment.


The story of my life, more or less.

Yes, this plot of Skittles is my homework—for an online class I’m taking through Coursera called Social Network Analysis. This was our first assignment: using a couple of free online tools to download your Facebook friends data and visualize your network of friends.

The ease with which I was able to complete the assignment made me thankful for how (relatively) accessible network science is as a field. Thanks to social network APIs and open-source software, the tools you need to analyze your own social data are easily available. Consider it the social networking version of 23andme—personal memomics, if you will. (And thanks to the NSA, awareness has gone up, too!)

Taking the graph of my network above, each circle is a friend of mine (known as a “node” in the parlance of graph theory) and each link (or “edge”) between nodes indicates they’re Facebook friends. Not all my friends are connected to all my other friends—there were some free-floating clusters. But for the sake of clarity, I’ve shown only the largest connected component (LCC) of my graph.

The spacing of the nodes is determined by an algorithm based on simple physics. In it, each node is repulsed from each other, like magnets. Each link is like a spring, tugging groups of people together based on their common friendships. Another algorithm detects these clusters, draws boundaries, and then assigns them colors. (I went through and annotated some of them.)

I want to emphasize that I’m not in the center of this graph—a so-called ego network. I’m not in the graph at all! Each link between nodes is a direct friendship between those individuals. It shows how my friends are connected to each other, not how connected I am to them. In other words, this a disclaimer to all my friends out there: You’re all awesome, and how central you are in the chart has nothing to do with how important you are to me!

So what can network science tell me? The analysis tool, called Gephi, calculates several standard network metrics. For example, the “average path length” across the network is 5.2. In other words, there are, on average, just over 4 degrees of separation between any two people in the network—indicating a “smaller” network than the famous “six degrees of separation” maxim. This pattern is replicated over Facebook as a whole—the company’s data team reported in 2011 that the entire network had an average of just 3.74 degrees of separation, and that it was decreasing each year. The world is shrinking.

But for me, the most fascinating aspect wasn’t the numbers, but simply zooming in and browsing through my graph. As a record of my social life, it’s a strange thing. It’s all there—my friendships, my relationships, my bridges I’ve burned. By tracing edges to their nodes, I can remember the moments that linked them together, the chance friendships that I intend to keep for a lifetime, forming structures like filaments of galaxies fanning out across the night sky.

Some of the most interesting connections are the ones that unexpectedly link clusters. A kid from high school who’s now a b-boy in Seoul. Or the person who subletted my room one summer in college and then ran into a classmate from grad school while they were visiting physics grad schools. The people who are connected that you had no idea knew each other.

And isn’t it funny how some of your best friends can be the smallest nodes?


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