Community detection in multiplex networks: A seed-centric approach

Manel Hmimida; Rushed Kanawati

doi:10.3934/nhm.2015.10.71

Article Contents

2015, Volume 10, Issue 1: 71-85. Doi: 10.3934/nhm.2015.10.71

This issue Previous Article A simple and bounded model of population dynamics for mutualistic networks Next Article Characterizing ethnic interactions from human communication patterns in Ivory Coast

Community detection in multiplex networks: A seed-centric approach

Manel Hmimida^1, and
Rushed Kanawati^2,

1.
DICEN-CNAM 292, rue St Martin, 75141 PARIS CEDEX 03, & & LIPN - CNRS UMR 7030, Paris
2.
PSC, UP13, LIPN, CNRS UMR 7030, Villetaneuse

Received: June 30, 2014

Revised: November 30, 2014

Published: February 2015

Abstract Related Papers Cited by

Abstract

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.

Keywords:

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

Citation:

Related Papers

Cited by

References

[1]	S. Alexander and G. Joydeep, Cluster ensembles a knowledge reuse framework for combining multiple partitions, The Journal of Machine Learning Research, 3 (2003), 583-617.doi: 10.1162/153244303321897735.
[2]	A. Amelio and C. Pizzuti, A cooperative evolutionary approach to learn communities in multilayer networks, 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.
[3]	F. Battiston, V. Nicosia and V. Latora, Structural measures for multiplex networks, Physical Review E, 89 (2014), 032804.doi: 10.1103/PhysRevE.89.032804.
[4]	M. Berlingerio, M. Coscia and F. Giannotti, Finding and characterizing communities in multidimensional networks, in 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2011, 490-494.doi: 10.1109/ASONAM.2011.104.
[5]	M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale and D. Pedreschi, Evolving networks: Eras and turning points, Intell. Data Anal., 17 (2013), 27-48.
[6]	M. Berlingerio, F. Pinelli and F. Calabrese, Abacus: frequent pattern mining-based community discovery in multidimensional networks, Data Mining and Knowledge Discovery, 27 (2013), 294-320.doi: 10.1007/s10618-013-0331-0.
[7]	V. D. Blondel, J.-l. Guillaume and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, 2008 (2008), P10008.doi: 10.1088/1742-5468/2008/10/P10008.
[8]	P. Brodka and P. Kazienko, Encyclopedia of Social Network Analysis and Mining, Ch. Multi-layered Social Networks, Springer, 2014.
[9]	P. Bródka, K. Skibicki, P. Kazienko and K. Musial, A degree centrality in multi-layered social network, in 2011 International Conference on Computational Aspects of Social Networks (CASoN), IEEE, 2011, 237-242.
[10]	D. Cai, Z. Shao, X. He, X. Yan and J. Han, Mining hidden community in heterogeneous social networks, in Proceedings of the 3rd International Workshop on Link Discovery, ACM, 2005, 58-65.doi: 10.1145/1134271.1134280.
[11]	E. Cozzo, M. Kivelä, M. De Domenico, A. Solé, A. Arenas, S. Gómez, M. A. Porter and Y. Moreno, Clustering coefficients in multiplex networks, CoRR, arXiv:1307.6780, 2013.
[12]	J. Dahlin and P. Svenson, Ensemble approaches for improving community detection methods, CoRR, arXiv:1309.0242, 2013.
[13]	M. De Domenico, A. Solé, S. Gómez and A. Arenas, Random walks on multiplex networks, CoRR, arXiv:1306.0519, 2013.
[14]	C. Dwork, R. Kumar, M. Naor and D. Sivakumar, Rank aggregation methods for the web, in Proceedings of the 10th International Conference on World Wide Web, ACM, 2001, 613-622.doi: 10.1145/371920.372165.
[15]	S. Fortunato, Community detection in graphs, Physics Reports, 486 (2010), 75-174.doi: 10.1016/j.physrep.2009.11.002.
[16]	B. H. Good, Y.-A. de Montjoye and A. Clauset, Performance of modularity maximization in practical contexts, Physical Review E, 81 (2010), 046106, 19pp.doi: 10.1103/PhysRevE.81.046106.
[17]	R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme and G. Stumme, Tag recommendations in social bookmarking systems, AI Communications, 21 (2008), 231-247.
[18]	R. Kanawati, YASCA: An ensemble-based approach for community detection in complex networks, 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.
[19]	P. Kazienko, P. Brodka and K. Musial, Individual neighbourhood exploration in complex multi-layered social network, 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.
[20]	M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno and M. A. Porter, Multilayer networks, preprint, arXiv:1309.7233, 2013.
[21]	S. Massoud, Coeurs Stables de Communautés dans les Graphes de Terrain, Ph.D thesis, 2012.
[22]	P. J. Mucha, T. Richardson, K. Macon, M. A. Porter and J.-P. Onnela, Community structure in time-dependent, multiscale, and multiplex networks, Science, 328 (2010), 876-878.doi: 10.1126/science.1184819.
[23]	P. J. Mucha, T. Richardson, K. Macon, M. A. Porter and J.-P. Onnela, Community structure in time-dependent, multiscale, and multiplex networks, Science, 328 (2010), 876-878.doi: 10.1126/science.1184819.
[24]	T. Murata, Modularity for heterogeneous networks, in Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, ACM, 2010, 129-134.doi: 10.1145/1810617.1810640.
[25]	A. Potgieter, R. J. E. Cooke, K. A. April and I. O. Osunmakinde, Temporality in link prediction: Understanding social complexity, Emergence: Complexity & Organization, 11 (2009), 69-83.
[26]	J. Reichardt and S. Bornholdt, Statistical mechanics of community detection, Physical Review E, 74 (2006), 016110, 14pp.doi: 10.1103/PhysRevE.74.016110.
[27]	K. Rushed, Seed-centric approaches for community detection in complex networks, in Social Computing and Social Media, Springer, 2014, 197-208.
[28]	D. Suthers, J. Fusco, P. Schank, K.-H. Chu and M. Schlager, Discovery of community structures in a heterogeneous professional online network, in 2013 46th Hawaii International Conference on System Sciences (HICSS), IEEE, 2013, 3262-3271.doi: 10.1109/HICSS.2013.179.
[29]	L. Tang and H. Liu, Community detection and mining in social media, Synthesis Lectures on Data Mining and Knowledge Discovery, 2 (2010), 1-137.doi: 10.2200/S00298ED1V01Y201009DMK003.
[30]	Y. Zied and K. Rushed, Licod: Leader-driven approach for community detection in complex networks, Vietnam Journal of Computer Science, (2014), p30.