Michael Wu, Ph.D. is Lithium's Principal Scientist of Analytics, digging into the complex dynamics of social interaction and online communities.
Curated by Beth Kanter
An explanation about social network analysis and some guidelines for intrepretation.
So the most important thing when reading a social graph is to find out what relationships are being represented by the edges. This is even more important than what the vertices represent, because for SNA, the entities represented by the vertices will usually be people. 99% of the graph metrics out there depend heavily on the edges, so if the edge relationships change, the metrics will also change.
For example, the simplest graph metric is the degree centrality, and it measures how many connections a vertex has. For example, there are seven black edges connecting to me on the friendship graph (figure 2a), so I have seven friends. But there are only five red edges connecting to me, so I have five colleagues. My degree centrality on the beer buddy graph (figure 2c) is three, so I only have three beer buddies. Degree centrality can be computed for all users in the graph, for example, Ryan's degree centrality on the badminton pal graph (figure 2d) is two.
The interpretation of the graph metric also depends on the edge relationship. So, you cannot say anything about how many colleagues I have based on the friendship graph (figure 2a), because the colleague relationship is not being represented in the friendship graph. Even if you assume that everyone I've worked with are my friends; using just the friendship graph, then number of colleagues I have can still be anywhere from zero to seven. Therefore, do not try to make any inference or conclusion based on a graph about any relationship that is not explicitly represented by the edges. If you do that, you might as well just flip a coin or make a random guess.