Fig. 2
From: CDLIB: a python library to extract, compare and evaluate communities from complex networks
Visual Analitycs. Analytics plots for CD algorithm. First line: (left) is the plot of the scores (in this case obtained with adjusted_mutual_information) obtained by a list of methods on a list of graphs (on LFR benchmark graphs (Lancichinetti et al. 2008)); (right) is the plot of the distribution of a property (e.g. size) among all communities for a clustering, or a list of clusterings on the Zachary’s Karate Club graph. Second line: (left) is the plot of the relation between the two functions size and internal_edge_density of CD algorithms on the Zachary’s Karate Club graph; (right) is the plot of the similarity matrix between a list of clusterings, using the provided scoring function (in this case obtained with adjusted_mutual_information) on the Zachary’s Karate Club graph