March  2015, 10(1): 71-85. doi: 10.3934/nhm.2015.10.71

Community detection in multiplex networks: A seed-centric approach

1. 

DICEN-CNAM 292, rue St Martin, 75141 PARIS CEDEX 03, & & LIPN - CNRS UMR 7030, Paris, France

2. 

PSC, UP13, LIPN, CNRS UMR 7030, Villetaneuse, France

Received  July 2014 Revised  December 2014 Published  February 2015

Multiplex network is an emergent model that has been lately proposed in order to cope with the complexity of real-world networks. A multiplex network is defined as a multi-layer interconnected graph. Each layer contains the same set of nodes but interconnected by different types of links. This rich representation model requires to redefine most of the existing network analysis algorithms. In this paper we focus on the central problem of community detection. Most of existing approaches consist on transforming the problem, in a way or another, to the classical setting of community detection in a monoplex network. In this work, we propose a new approach that consists on adapting a seed-centric algorithm to the multiplex case. The first experiments on heterogeneous bibliographical networks show the relevance of the approach compared to the existing algorithms.
Citation: Manel Hmimida, Rushed Kanawati. Community detection in multiplex networks: A seed-centric approach. Networks & Heterogeneous Media, 2015, 10 (1) : 71-85. doi: 10.3934/nhm.2015.10.71
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show all references

References:
[1]

The Journal of Machine Learning Research, 3 (2003), 583-617. doi: 10.1162/153244303321897735.  Google Scholar

[2]

in Parallel Problem Solving from Nature-PPSN XIII, Lecture Notes in Computer Science, 8672 Springer International Publishing, Switzerland, 2014, 222-232. doi: 10.1007/978-3-319-10762-2_22.  Google Scholar

[3]

Physical Review E, 89 (2014), 032804. doi: 10.1103/PhysRevE.89.032804.  Google Scholar

[4]

in 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2011, 490-494. doi: 10.1109/ASONAM.2011.104.  Google Scholar

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Intell. Data Anal., 17 (2013), 27-48. Google Scholar

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Data Mining and Knowledge Discovery, 27 (2013), 294-320. doi: 10.1007/s10618-013-0331-0.  Google Scholar

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Journal of Statistical Mechanics: Theory and Experiment, 2008 (2008), P10008. doi: 10.1088/1742-5468/2008/10/P10008.  Google Scholar

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Springer, 2014. Google Scholar

[9]

in 2011 International Conference on Computational Aspects of Social Networks (CASoN), IEEE, 2011, 237-242. Google Scholar

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in Proceedings of the 3rd International Workshop on Link Discovery, ACM, 2005, 58-65. doi: 10.1145/1134271.1134280.  Google Scholar

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in Proceedings of the 10th International Conference on World Wide Web, ACM, 2001, 613-622. doi: 10.1145/371920.372165.  Google Scholar

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[16]

Physical Review E, 81 (2010), 046106, 19pp. doi: 10.1103/PhysRevE.81.046106.  Google Scholar

[17]

AI Communications, 21 (2008), 231-247.  Google Scholar

[18]

in Computing and Combinatorics, Lecture Notes in Computer Science, 8591, Springer International Publishing, Switzerland, 2014, 657-666. doi: 10.1007/978-3-319-08783-2_57.  Google Scholar

[19]

in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol. 3, IEEE, 2010, 5-8. doi: 10.1109/WI-IAT.2010.313.  Google Scholar

[20]

preprint, arXiv:1309.7233, 2013. Google Scholar

[21]

Ph.D thesis, 2012. Google Scholar

[22]

Science, 328 (2010), 876-878. doi: 10.1126/science.1184819.  Google Scholar

[23]

Science, 328 (2010), 876-878. doi: 10.1126/science.1184819.  Google Scholar

[24]

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[25]

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