Oualid Boutemine
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Abstract
We investigate the problem of community detection in multidimensional networks, that is, networks where entities engage in various interaction types (dimensions) simultaneously. While some approaches have been proposed to identify community structures in multidimensional networks, there are a number of problems still to solve. In fact, the majority of the proposed approaches suffer from one or even more of the following limitations: (1) difficulty detecting communities in networks characterized by the presence of many irrelevant dimensions, (2) lack of systematic procedures to explicitly identify the relevant dimensions of each community, and (3) dependence on a set of user-supplied parameters, including the number of communities, that require a proper tuning. Most of the existing approaches are inadequate for dealing with these three issues in a unified framework. In this paper, we develop a novel approach that is capable of addressing the aforementioned limitations in a single framework. The proposed approach allows automated identification of communities and their sub-dimensional spaces using a novel objective function and a constrained label propagation-based optimization strategy. By leveraging the relevance of dimensions at the node level, the strategy aims to maximize the number of relevant within-community links while keeping track of the most relevant dimensions. A notable feature of the proposed approach is that it is able to automatically identify low dimensional community structures embedded in a high dimensional space. Experiments on synthetic and real multidimensional networks illustrate the suitability of the new method.
- Alessia Amelio and Clara Pizzuti. 2014. A cooperative evolutionary approach to learn communities in multilayer networks. In Proceedings of Parallel Problem Solving from Nature - PPSN XIII. Lecture Notes in Computer Science, vol. 8672, Springer, 222--232.Google Scholar
- Michael J. Barber and John W. Clark. 2009. Detecting network communities by propagating labels under constraints. Physical Review E 80, 2 (2009), 026129.Google ScholarCross Ref
- Federico Battiston, Vincenzo Nicosia, and Vito Latora. 2014. Structural measures for multiplex networks. Physical Review E 89, 3 (2014), 032804.Google ScholarCross Ref
- Michele Berlingerio, Michele Coscia, and Fosca Giannotti. 2011. Finding and characterizing communities in multidimensional networks. In Proceedings of the IEEE International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2011). 490--494. Google ScholarDigital Library
- Michele Berlingerio, Michele Coscia, Fosca Giannotti, Anna Monreale, and Dino Pedreschi. 2013. Multidimensional networks: Foundations of structural analysis. World Wide Web 16, 5--6 (2013), 567--593. Google ScholarDigital Library
- Michele Berlingerio, Fabio Pinelli, and Francesco Calabrese. 2013. ABACUS: Frequent pAttern mining-based community discovery in multidimensional networks. Data Mining and Knowledge Discovery 27, 3 (2013), 294--320.Google ScholarCross Ref
- Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10 (2008), P10008.Google ScholarCross Ref
- Brigitte Boden, Stephan Günnemann, Holger Hoffmann, and Thomas Seidl. 2012. Mining coherent subgraphs in multi-layer graphs with edge labels. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’2012). 1258--1266. Google ScholarDigital Library
- Christian Borgelt. 2003. Efficient implementations of apriori and eclat. In Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI’2003).Google Scholar
- Mohamed Bouguessa, Shengrui Wang, and Benoit Dumoulin. 2010. Discovering knowledge-sharing communities in question-answering forums. ACM Transactions on Knowledge Discovery from Data 5, 1, Article 3 (Dec 2010), 49 pages. Google ScholarDigital Library
- Deng Cai, Zheng Shao, Xiaofei He, Xifeng Yan, and Jiawei Han. 2005. Mining hidden community in heterogeneous social networks. In Proceedings of the 3rd ACM SIGKDD International Workshop on Link Discovery (LinkKDD’2005). 58--65. Google ScholarDigital Library
- Vincenza Carchiolo, Alessandro Longheu, Michele Malgeri, and Giuseppe Mangioni. 2011. Communities unfolding in multislice networks. In Complex Networks. Communications in Computer and Information Science. Springer, 187--195.Google Scholar
- Jingchun Chen and Bo Yuan. 2006. Detecting functional modules in the yeast protein-protein interaction network. Bioinformatics 22, 18 (2006), 2283--2290. Google ScholarDigital Library
- Anne Condon and Richard M. Karp. 2001. Algorithms for graph partitioning on the planted partition model. Random Structures and Algorithms 18, 2 (2001), 116--140. Google ScholarDigital Library
- Michele Coscia, Fosca Giannotti, and Dino Pedreschi. 2011. A classification for community discovery methods in complex networks. Statistical Analysis and Data Mining: The ASA Data Science Journal 4, 5 (2011), 512--546. Google ScholarDigital Library
- Michele Coscia, Giulio Rossetti, Fosca Giannotti, and Dino Pedreschi. 2014. Uncovering hierarchical and overlapping communities with a local-first approach. ACM Transactions on Knowledge Discovery from Data 9, 1, Article 6 (Aug. 2014), 27 pages. Google ScholarDigital Library
- Manlio De Domenico, Andrea Lancichinetti, Alex Arenas, and Martin Rosvall. 2015. Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Physical Review X 5, 1 (2015), 011027.Google Scholar
- Manlio De Domenico, Vincenzo Nicosia, Alexandre Arenas, and Vito Latora. 2015. Structural reducibility of multilayer networks. Nature Communications 6 (2015).Google Scholar
- Manlio De Domenico, Albert Sole, Sergio Gomez, and Alex Arenas. 2014. Navigability of interconnected networks under random failures. Proceedings of the National Academy of Sciences of the United States of America 111, 23, 8351--8356.Google ScholarCross Ref
- Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, and Nikolai Nefedov. 2012. Clustering with multi-layer graphs: A spectral perspective. IEEE Transactions on Signal Processing 60, 11 (2012), 5820--5831. Google ScholarDigital Library
- Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, and Nikolai Nefedov. 2014. Clustering on multi-layer graphs via subspace analysis on grassmann manifolds. IEEE Transactions on Signal Processing 62, 4 (2014), 905--918. Google ScholarDigital Library
- Daniel M. Dunlavy, Tamara G. Kolda, and W. Philip Kegelmeyer. 2011. Multilinear algebra for analyzing data with multiple linkages. In Graph Algorithms in the Language of Linear Algebra. J. Kepner and J. Gilbert (Eds.), Fundamentals of Algorithms, SIAM, Philadelphia (2011), 85--114.Google Scholar
- Santo Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3 (2010), 75--174.Google ScholarCross Ref
- Edward B. Fowlkes and Colin L. Mallows. 1983. A method for comparing two hierarchical clusterings. Journal of the American Statistical Association 78, 383 (1983), 553--569.Google ScholarCross Ref
- A. Graovac, I. Gutman, N. Trinajstić, and T. Živković. 1972. Graph theory and molecular orbitals. Theoretica Chimica Acta 26, 1 (1972), 67--78.Google ScholarCross Ref
- Manel Hmimida and Rushed Kanawati. 2015. Community detection in multiplex networks: A seed-centric approach. Networks and Heterogeneous Media 10, 1 (2015), 71--85.Google ScholarCross Ref
- Lawrence Hubert and Phipps Arabie. 1985. Comparing partitions. Journal of Classification 2, 1 (1985), 193--218.Google ScholarCross Ref
- Przemyslaw Kazienko, Katarzyna Musial, Elzbieta Kukla, Tomasz Kajdanowicz, and Piotr Brodka. 2011. Multidimensional social network: Model and analysis. In Computational Collective Intelligence. Technologies and Applications. Lecture Notes in Computer Science, vol. 6922, Springer, 378--387. Google ScholarDigital Library
- Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Review 51, 3 (2009), 455--500. Google ScholarDigital Library
- Jeremy Kun, Rajmonda Caceres, and Kevin Carter. 2014. Locally boosted graph aggregation for community detection. arXiv preprint arXiv:1405.3210.Google Scholar
- Zhana Kuncheva and Giovanni Montana. 2015. Community detection in multiplex networks using locally adaptive random walks. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2015). ACM, 1308--1315. Google ScholarDigital Library
- Renaud Lambiotte, J.-C. Delvenne, and Mauricio Barahona. 2014. Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks. IEEE Transactions on Network Science and Engineering 1, 2 (2014), 76--90.Google ScholarCross Ref
- Ian X. Y. Leung, Pan Hui, Pietro Lio, and Jon Crowcroft. 2009. Towards real-time community detection in large networks. Physical Review E 79, 6 (2009), 066107.Google ScholarCross Ref
- Xutao Li, Michael K. Ng, and Yunming Ye. 2014. Multicomm: Finding community structure in multi-dimensional networks. IEEE Transactions on Knowledge and Data Engineering 26, 4 (2014), 929--941. Google ScholarDigital Library
- Guimei Liu and Limsoon Wong. 2008. Effective pruning techniques for mining quasi-cliques. In Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, Springer, 33--49.Google ScholarCross Ref
- Muhuo Liu and Bolian Liu. 2009. New sharp upper bounds for the first Zagreb index. Communications in Mathematical and in Computer Chemistry 62, 3 (2009), 689--698.Google Scholar
- Xinhai Liu, Shuiwang Ji, Wolfgang Glanzel, and Bart De Moor. 2013. Multiview partitioning via tensor methods. IEEE Transactions on Knowledge and Data Engineering 25, 5 (2013), 1056--1069. Google ScholarDigital Library
- Xin Liu and Tsuyoshi Murata. 2010. Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Physica A: Statistical Mechanics and Its Applications 389, 7 (2010), 1493--1500.Google ScholarCross Ref
- Chuan Wen Loe and Henrik Jeldtoft Jensen. 2015. Comparison of communities detection algorithms for multiplex. Physica A: Statistical Mechanics and Its Applications 431 (2015), 29--45.Google ScholarCross Ref
- Matteo Magnani, Barbora Micenkova, and Luca Rossi. 2013. Combinatorial analysis of multiple networks. arXiv preprint arXiv:1303.4986.Google Scholar
- Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press. Google ScholarDigital Library
- Peter J. Mucha, Thomas Richardson, Kevin Macon, Mason A. Porter, and Jukka-Pekka Onnela. 2010. Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 5980 (2010), 876--878.Google Scholar
- Mark E. J. Newman. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 23 (2006), 8577--8582.Google ScholarCross Ref
- Vincenzo Nicosia and Vito Latora. 2015. Measuring and modelling correlations in multiplex networks. Physical Review E 92, 3 (2015), 032805.Google ScholarCross Ref
- Evangelos E. Papalexakis, Leman Akoglu, and D. Ience. 2013. Do more views of a graph help? Community detection and clustering in multi-graphs. In Proceedings of the16th IEEE International Conference on Information Fusion (FUSION’2013). 899--905.Google Scholar
- Pascal Pons and Matthieu Latapy. 2005. Computing communities in large networks using random walks. In Computer and Information Sciences-ISCIS 2005. Lecture Notes in Computer Science, vol. 3733, Springer, 284--293. Google ScholarDigital Library
- Usha Nandini Raghavan, Rèka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76, 3 (2007), 036106.Google ScholarCross Ref
- Martin Rosvall and Carl T. Bergstrom. 2008. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences 105, 4 (2008), 1118--1123.Google ScholarCross Ref
- Sucheta Soundarajan and John E. Hopcroft. 2015. Use of local group information to identify communities in networks. ACM Transactions on Knowledge Discovery from Data 9, 3, Article 21 (April 2015), 27 pages. Google ScholarDigital Library
- Chris Stark, Bobby-Joe Breitkreutz, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, and Mike Tyers. 2006. BioGRID: A general repository for interaction datasets. Nucleic Acids Research 34, suppl 1 (2006), D535--D539.Google ScholarCross Ref
- Alexander Strehl and Joydeep Ghosh. 2003. Cluster ensembles—A knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research 3 (2003), 583--617. Google ScholarDigital Library
- Lei Tang, Xufei Wang, and Huan Liu. 2009. Uncoverning groups via heterogeneous interaction analysis. In Proceedings of the 9th IEEE International Conference on Data Mining (ICDM’2009). 503--512. Google ScholarDigital Library
- Lei Tang, Xufei Wang, and Huan Liu. 2012. Community detection via heterogeneous interaction analysis. Data Mining and Knowledge Discovery 25, 1 (2012), 1--33. Google ScholarDigital Library
- Wei Tang, Zhengdong Lu, and Inderjit S. Dhillon. 2009. Clustering with multiple graphs. In Proceedings of the 9th IEEE International Conference on Data Mining (ICDM’2009). 1016--1021. Google ScholarDigital Library
- Aidong Zhang. 2009. Protein Interaction Networks: Computational Analysis. Cambridge University Press. Google ScholarDigital Library
Index Terms
- Mining Community Structures in Multidimensional Networks
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