Figure 2

Disjoint cluster detection performance.

(A) The LFR benchmarks track cluster recovery as networks become increasingly cross-linked (as μ increases) for γ (cluster size distribution parameter) equal to 2 and β (within-cluster degree distribution parameter) equal to 1. Several metrics characterize cluster recovery with varying levels of sensitivity. For the following measures (min = 0), lower values indicate better alignment between the true partition and partition generated by SpeakEasy: NVD - Normalized Van Dongen metric. For the following measures, larger values (max = 1) indicate better alignment between the true and SpeakEasy partitions: NMI - Normalized Mutual Information; F-measure; RI- Rand Index; ARI - Adjusted Rand Index; JI - Jaccard Index. See Chen et al.³⁴ for additional details on these statistical measures. (B) These modularity values provide a statistical estimate of the separation between clusters. For both Q (modularity) and Q_ds (modularity density), larger values (max = 1) indicate better community separation. (C) Recovery of true clusters quantified by NMI as a function of μ (cross-linking between clusters) and O_m (number of communities associated with each multi-community node). (D) F(multi)-score is the standard F-score, but specifically applied for detection of correct community associations of multi-community nodes, calculated at various values of O_m and different average connectivity levels (D = 10,20). NMI metric used for overlapping communities (panels C,D) does not reduce to disjoint NMI, so NMI scores for O_m = 1, cannot be directly compared to panel A.