Node-Centric Community Detection in Multilayer Networks with Layer-Coverage Diversification Bias

Abstract : The problem of node-centric, or local, community detection in information networks refers to the identification of a community for a given input node, having limited information about the network topology. Existing methods for solving this problem, however, are not conceived to work on complex networks. In this paper, we propose a novel framework for local community detection based on the multilayer network model. Our approach relies on the maximization of the ratio between the community internal connection density and the external connection density , according to multilayer similarity-based community relations. We also define a biasing scheme that allows the discovery of local communities characterized by different degrees of layer-coverage diversification. Experimental evaluation conducted on real-world multilayer networks has shown the significance of our approach.
Type de document :
Communication dans un congrès
CompleNet, Mar 2017, Dubrovnik, Croatia. 8th International Workshop on Complex Networks, pp.57-66, 2017, Complex Networks VIII. 〈https://complenet17.weebly.com〉. 〈10.1007/978-3-319-54241-6_5〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01912004
Contributeur : Arnaud Sallaberry <>
Soumis le : lundi 5 novembre 2018 - 08:24:15
Dernière modification le : samedi 17 novembre 2018 - 16:50:54
Document(s) archivé(s) le : mercredi 6 février 2019 - 14:47:55

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Roberto Interdonato, Andrea Tagarelli, Dino Ienco, Arnaud Sallaberry, Pascal Poncelet. Node-Centric Community Detection in Multilayer Networks with Layer-Coverage Diversification Bias. CompleNet, Mar 2017, Dubrovnik, Croatia. 8th International Workshop on Complex Networks, pp.57-66, 2017, Complex Networks VIII. 〈https://complenet17.weebly.com〉. 〈10.1007/978-3-319-54241-6_5〉. 〈lirmm-01912004〉

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