OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks

Abstract : Community structure is one of the most prominent features of complex networks. Community structure detection is of great importance to provide insights into the network structure and functionalities. Most proposals focus on static networks. However, finding communities in a dynamic network is even more challenging, especially when communities overlap with each other. In this article , we present an online algorithm, called OLCPM, based on clique percolation and label propagation methods. OLCPM can detect overlapping communities and works on temporal networks with a fine granularity. By locally updating the community structure, OLCPM delivers significant improvement in running time compared with previous clique percolation techniques. The experimental results on both synthetic and real-world networks illustrate the effectiveness of the method.
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Computer Communications, Elsevier, In press, 〈10.1016/j.comcom.2018.04.003〉
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https://hal.archives-ouvertes.fr/hal-01761341
Contributeur : Remy Cazabet <>
Soumis le : mardi 10 avril 2018 - 12:53:40
Dernière modification le : vendredi 11 mai 2018 - 15:04:02

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Souâad Boudebza, Rémy Cazabet, Faiçal Azouaou, Omar Nouali. OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks. Computer Communications, Elsevier, In press, 〈10.1016/j.comcom.2018.04.003〉. 〈hal-01761341〉

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