ComSim : A bipartite community detection algorithm using cycle and node's similarity

Raphael Tackx 1 Fabien Tarissan Jean-Loup Guillaume
1 ComplexNetworks
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This study proposes ComSim, a new algorithm to detect communities in bipartite networks. This approach generates a partition of nodes by relying on similarity between the nodes in terms of links towards ⊥ nodes. In order to show the relevance of this approach, we implemented and tested the algorithm on 2 small datasets equipped with a ground-truth partition of the nodes. It turns out that, compared to 3 baseline algorithms used in the context of bipartite graph, ComSim proposes the best communities. In addition, we tested the algorithm on a large scale network. Results show that ComSim has good performances, close in time to Louvain. Besides, a qualitative investigation of the communities detected by ComSim reveals that it proposes more balanced communities.
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Communication dans un congrès
Complex Networks 2017, Nov 2017, Lyon, France. Springer, Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), 689, pp.278-289, 2018, Studies in Computational Intelligence. 〈10.1007/978-3-319-72150-7_23〉
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Dernière modification le : jeudi 22 novembre 2018 - 14:23:17

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Raphael Tackx, Fabien Tarissan, Jean-Loup Guillaume. ComSim : A bipartite community detection algorithm using cycle and node's similarity. Complex Networks 2017, Nov 2017, Lyon, France. Springer, Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), 689, pp.278-289, 2018, Studies in Computational Intelligence. 〈10.1007/978-3-319-72150-7_23〉. 〈hal-01657093〉

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