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Exercise

Comparing community algorithms

There are many ways that you can find a community in a graph (you can read more about them here). Unfortunately, different community detection algorithms will give different results, and the best algorithm to choose depends on some of the properties of your graph Yang et. al..

You can compare the resulting communities usingcompare(). This returns a score ("the variance in information"), which counts whether or not any two vertices are members of the same community. A lower score means that the two community structures are more similar.

You can see if two vertices are in the same community using membership(). If the vertices have the same membership number, then they are in the same community.

Instructions 1/3
XP
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  • Use as.undirected() to make the graph undirected.
  • Use three clustering algorithms on retweet_graph_undir to identify communities: cluster_fast_greedy(), cluster_infomap(), and cluster_louvain().