Entropy based community detection in augmented social networks

Juan David Cruz Gomez 1, 2 Cécile Bothorel 1, 2 François Poulet 3
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Social network analysis has become a major subject in recent times, bringing also several challenges in the computer science field. One aspect of the social network analysis is the community detection problem, which is seen as a graph clustering problem. However, social networks are more than a graph, they have an interesting amount of information derived from its social aspect, such as profile information, content sharing and annotations, among others. Most of the community detection algorithms use only the structure of the network, i.e., the graph. In this paper we propose a new method which uses the semantic information along with the network structure in the community detection process. Thus, our method combines an algorithm for optimizing modularity and an entropy-based data clustering algorithm, which tries to find a partition with low entropy and keeping in mind the modularity.
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Submitted on : Monday, November 14, 2011 - 10:54:58 AM
Last modification on : Thursday, October 17, 2019 - 12:36:43 PM


  • HAL Id : hal-00640722, version 1


Juan David Cruz Gomez, Cécile Bothorel, François Poulet. Entropy based community detection in augmented social networks. International Conference on Computational Aspects of Social Networks, Oct 2011, Salamanca, Spain. pp.163-168. ⟨hal-00640722⟩



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