Mining time-dependent communities

Qinna Wang 1, 2 Eric Fleury 1, 2
1 DNET - Dynamic Networks
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : Time evolution is one important feature of communities in network science. It is related with capturing critical events, characterizing community members, and predicting behaviours of communities in networks with time varying. However, most of existing community detection techniques are proposed for static networks. Here, we present a new framework to uncover community structure for each temporal graph over time. In consideration of regularizing time-dependent communities, the high temporal variations will be prevented and the gained results on community evolution become more reasonable. Having applied it on synthetic networks, the experimental results offer new views in dynamic networks
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Communication dans un congrès
LAWDN - Latin-American Workshop on Dynamic Networks, Nov 2010, Buenos Aires, Argentina. 4 p., 2010
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Qinna Wang, Eric Fleury. Mining time-dependent communities. LAWDN - Latin-American Workshop on Dynamic Networks, Nov 2010, Buenos Aires, Argentina. 4 p., 2010. 〈inria-00531735〉

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