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
References:
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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), (2011), 490.  doi: 10.1109/ASONAM.2011.104.  Google Scholar

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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).  doi: 10.1088/1742-5468/2008/10/P10008.  Google Scholar

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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), (2011), 237.   Google Scholar

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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, (2005), 58.  doi: 10.1145/1134271.1134280.  Google Scholar

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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, (2013).   Google Scholar

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J. Dahlin and P. Svenson, Ensemble approaches for improving community detection methods,, CoRR, (2013).   Google Scholar

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M. De Domenico, A. Solé, S. Gómez and A. Arenas, Random walks on multiplex networks,, CoRR, (2013).   Google Scholar

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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, (2001), 613.  doi: 10.1145/371920.372165.  Google Scholar

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S. Fortunato, Community detection in graphs,, Physics Reports, 486 (2010), 75.  doi: 10.1016/j.physrep.2009.11.002.  Google Scholar

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B. H. Good, Y.-A. de Montjoye and A. Clauset, Performance of modularity maximization in practical contexts,, Physical Review E, 81 (2010).  doi: 10.1103/PhysRevE.81.046106.  Google Scholar

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R. Kanawati, YASCA: An ensemble-based approach for community detection in complex networks,, in Computing and Combinatorics, (8591), 657.  doi: 10.1007/978-3-319-08783-2_57.  Google Scholar

[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), (2010), 5.  doi: 10.1109/WI-IAT.2010.313.  Google Scholar

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S. Massoud, Coeurs Stables de Communautés dans les Graphes de Terrain,, Ph.D thesis, (2012).   Google Scholar

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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.  doi: 10.1126/science.1184819.  Google Scholar

[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.  doi: 10.1126/science.1184819.  Google Scholar

[24]

T. Murata, Modularity for heterogeneous networks,, in Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, (2010), 129.  doi: 10.1145/1810617.1810640.  Google Scholar

[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.   Google Scholar

[26]

J. Reichardt and S. Bornholdt, Statistical mechanics of community detection,, Physical Review E, 74 (2006).  doi: 10.1103/PhysRevE.74.016110.  Google Scholar

[27]

K. Rushed, Seed-centric approaches for community detection in complex networks,, in Social Computing and Social Media, (2014), 197.   Google Scholar

[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), (2013), 3262.  doi: 10.1109/HICSS.2013.179.  Google Scholar

[29]

L. Tang and H. Liu, Community detection and mining in social media,, Synthesis Lectures on Data Mining and Knowledge Discovery, 2 (2010), 1.  doi: 10.2200/S00298ED1V01Y201009DMK003.  Google Scholar

[30]

Y. Zied and K. Rushed, Licod: Leader-driven approach for community detection in complex networks,, Vietnam Journal of Computer Science, (2014).   Google Scholar

show all references

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.  doi: 10.1162/153244303321897735.  Google Scholar

[2]

A. Amelio and C. Pizzuti, A cooperative evolutionary approach to learn communities in multilayer networks,, in Parallel Problem Solving from Nature-PPSN XIII, (8672), 222.  doi: 10.1007/978-3-319-10762-2_22.  Google Scholar

[3]

F. Battiston, V. Nicosia and V. Latora, Structural measures for multiplex networks,, Physical Review E, 89 (2014).  doi: 10.1103/PhysRevE.89.032804.  Google Scholar

[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), (2011), 490.  doi: 10.1109/ASONAM.2011.104.  Google Scholar

[5]

M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale and D. Pedreschi, Evolving networks: Eras and turning points,, Intell. Data Anal., 17 (2013), 27.   Google Scholar

[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.  doi: 10.1007/s10618-013-0331-0.  Google Scholar

[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).  doi: 10.1088/1742-5468/2008/10/P10008.  Google Scholar

[8]

P. Brodka and P. Kazienko, Encyclopedia of Social Network Analysis and Mining, Ch. Multi-layered Social Networks,, Springer, (2014).   Google Scholar

[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), (2011), 237.   Google Scholar

[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, (2005), 58.  doi: 10.1145/1134271.1134280.  Google Scholar

[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, (2013).   Google Scholar

[12]

J. Dahlin and P. Svenson, Ensemble approaches for improving community detection methods,, CoRR, (2013).   Google Scholar

[13]

M. De Domenico, A. Solé, S. Gómez and A. Arenas, Random walks on multiplex networks,, CoRR, (2013).   Google Scholar

[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, (2001), 613.  doi: 10.1145/371920.372165.  Google Scholar

[15]

S. Fortunato, Community detection in graphs,, Physics Reports, 486 (2010), 75.  doi: 10.1016/j.physrep.2009.11.002.  Google Scholar

[16]

B. H. Good, Y.-A. de Montjoye and A. Clauset, Performance of modularity maximization in practical contexts,, Physical Review E, 81 (2010).  doi: 10.1103/PhysRevE.81.046106.  Google Scholar

[17]

R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme and G. Stumme, Tag recommendations in social bookmarking systems,, AI Communications, 21 (2008), 231.   Google Scholar

[18]

R. Kanawati, YASCA: An ensemble-based approach for community detection in complex networks,, in Computing and Combinatorics, (8591), 657.  doi: 10.1007/978-3-319-08783-2_57.  Google Scholar

[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), (2010), 5.  doi: 10.1109/WI-IAT.2010.313.  Google Scholar

[20]

M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno and M. A. Porter, Multilayer networks,, preprint, (2013).   Google Scholar

[21]

S. Massoud, Coeurs Stables de Communautés dans les Graphes de Terrain,, Ph.D thesis, (2012).   Google Scholar

[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.  doi: 10.1126/science.1184819.  Google Scholar

[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.  doi: 10.1126/science.1184819.  Google Scholar

[24]

T. Murata, Modularity for heterogeneous networks,, in Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, (2010), 129.  doi: 10.1145/1810617.1810640.  Google Scholar

[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.   Google Scholar

[26]

J. Reichardt and S. Bornholdt, Statistical mechanics of community detection,, Physical Review E, 74 (2006).  doi: 10.1103/PhysRevE.74.016110.  Google Scholar

[27]

K. Rushed, Seed-centric approaches for community detection in complex networks,, in Social Computing and Social Media, (2014), 197.   Google Scholar

[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), (2013), 3262.  doi: 10.1109/HICSS.2013.179.  Google Scholar

[29]

L. Tang and H. Liu, Community detection and mining in social media,, Synthesis Lectures on Data Mining and Knowledge Discovery, 2 (2010), 1.  doi: 10.2200/S00298ED1V01Y201009DMK003.  Google Scholar

[30]

Y. Zied and K. Rushed, Licod: Leader-driven approach for community detection in complex networks,, Vietnam Journal of Computer Science, (2014).   Google Scholar

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