On the separability of structural classes of communities

Three major factors govern the intricacies of community extraction in networks: (1) the application domain includes a wide variety of networks of fundamentally different natures, (2) the literature offers a multitude of disparate community detection algorithms, and (3) there is no consensus characterizing how to discriminate communities from noncommunities. In this paper, we present a comprehensive analysis of community properties through a class separability framework. Our approach enables the assessement of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. To demostrate this concept, we furnish our method with a large set of structural properties and multiple community detection algorithms. Applied to a diverse collection of large scale network datasets, the analysis reveals that (1) the different detection algorithms extract fundamentally different structures; (2)...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Bruno D. Abrahao, Sucheta Soundarajan, John E. Hopcroft, Robert Kleinberg
• Keywords: Data Mining, Detection Algorithms, Disparate Community, KDD 2012, Structure Detection