On Accuracy of Community Structure Discovery Algorithms

Günce Keziban Orman (1, 2) , Vincent Labatut (3) , Hocine Cherifi (1)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
2 BIT Lab - BIT lab
3 Bit Lab

Community structure discovery in complex networks is a quite challenging problem spanning many applications in various disciplines such as biology, social network and physics. Emerging from various approaches numerous algorithms have been proposed to tackle this problem. Nevertheless little attention has been devoted to compare their efficiency on realistic simulated data. To better understand their relative performances, we evaluate systematically eleven algorithms covering the main approaches. The Normalized Mutual Information (NMI) measure is used to assess the quality of the discovered community structure from controlled artificial networks with realistic topological properties. Results show that along with the network size, the average proportion of intra-community to inter-community links is the most influential parameter on performances. Overall, "Infomap" is the leading algorithm, followed by "Walktrap", "SpinGlass" and "Louvain" which also achieve good consistency.

Mots clés: Complex Networks; Community Structure; Benchmark Graphs; Normalized Mutual Information; Community detection

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Réseaux et télécommunications [cs.NI]

Günce Keziban Orman, Vincent Labatut, Hocine Cherifi. On Accuracy of Community Structure Discovery Algorithms. Journal of Convergence Information Technology, 2011, 6 (11), pp.283, 292. ⟨hal-00653084⟩