Une nouvelle mesure pour l'\'evaluation des m\'ethodes de d\'etection de communaut\'es
Vincent Labatut

TL;DR
This paper introduces a new evaluation measure for community detection in complex networks, addressing limitations of existing tools by better utilizing available information, and demonstrates its application on realistic network data.
Contribution
It proposes a modified measure for evaluating community detection algorithms, improving the relevance of assessments in network analysis.
Findings
The new measure provides more accurate evaluation of community detection.
Application on real networks shows improved interpretability.
The method highlights limitations of previous evaluation tools.
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 a part of the available information, and can therefore lead to incorrect interpretations. In this article, we illustrate this limitation, and propose a solution by modifying an existing measure. We then apply it to realistic community-structured networks, in order to perform a first evaluation.---La d\'etection de communaut\'es dans un r\'eseau complexe est une t\^ache que l'on peut rapprocher de la classification non-supervis\'ee r\'ealis\'ee en fouille de donn\'ees classique. Pour cette raison, l'\'evaluation des algorithmes accomplissant ce type de traitement…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Clustering Algorithms Research · Opinion Dynamics and Social Influence
