Generalized Measures for the Evaluation of Community Detection Methods - Archive ouverte HAL Access content directly
Journal Articles International Journal of Social Network Analysis and Mining (SNAM) Year : 2015

Generalized Measures for the Evaluation of Community Detection Methods

Vincent Labatut

Abstract

Community detection can be considered as a variant of cluster analysis applied to complex networks. For this reason, all existing studies have been using tools derived from this field when evaluating community detection algorithms. However, those are not completely relevant in the context of network analysis, because they ignore an essential part of the available information: the network structure. Therefore, they can lead to incorrect interpretations. In this article, we review these measures, and illustrate this limitation. We propose a modification to solve this problem, and apply it to the three most widespread measures: purity, Rand index and normalized mutual information (NMI). We then perform an experimental evaluation on artificially generated networks with realistic community structure. We assess the relevance of the modified measures by comparison with their traditional counterparts, and also relatively to the topological properties of the community structures. On these data, the modified NMI turns out to provide the most relevant results.
Fichier principal
Vignette du fichier
Labatut2015.pdf (1.11 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00802923 , version 1 (20-03-2013)
hal-00802923 , version 2 (13-05-2016)

Licence

Attribution - NonCommercial - NoDerivatives

Identifiers

Cite

Vincent Labatut. Generalized Measures for the Evaluation of Community Detection Methods. International Journal of Social Network Analysis and Mining (SNAM), 2015, 2 (1), pp.44-63. ⟨10.1504/IJSNM.2015.069776⟩. ⟨hal-00802923v2⟩

Collections

UNIV-AVIGNON LIA
266 View
614 Download

Altmetric

Share

Gmail Facebook X LinkedIn More