Multiobjective approach for detecting communities in heterogeneous networks

Online social networks have a strong potential to be divided into a number of dense substructures, called communities. In such heterogeneous networks, the communities refer not only to dense parts of links but also to clusters present among other dimensions such as users' profiles, comments, and information flows. To find communities in these networks, researchers have developed a number of methods; however, to the best of the authors' knowledge, these methods are limited in taking only 2 dimensions into account, and they are also not able to give a sense of how users behave in their communities. To deal with these issues, this paper proposes a multiobjective optimization model in which a specific objective function has been used for each considered dimension in a given network. Because of the NP‐hardness of the studied problem, an efficient and effective multiobjective metaheuristic algorithm has been developed. By juxtaposing the nondominated solutions obtained, the proposed algorithm can demonstrate how users behave in their communities. To illustrate the effectiveness of the algorithm, a set of experiments with a comprehensive evaluation method is provided. The results show the superiority and the stability of the proposed algorithm.

[1]  Huan Liu,et al.  Identifying Evolving Groups in Dynamic Multimode Networks , 2012, IEEE Transactions on Knowledge and Data Engineering.

[2]  Prospero C. Naval,et al.  An effective use of crowding distance in multiobjective particle swarm optimization , 2005, GECCO '05.

[3]  Li Pan,et al.  Multi-objective community detection method by integrating users' behavior attributes , 2016, Neurocomputing.

[4]  Michael J. Freedman,et al.  Scalable Inference of Overlapping Communities , 2012, NIPS.

[5]  Ling Liu,et al.  Social influence based clustering of heterogeneous information networks , 2013, KDD.

[6]  Nicola Barbieri,et al.  Cascade-based community detection , 2013, WSDM.

[7]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[8]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Yizhou Sun,et al.  Ranking-based clustering of heterogeneous information networks with star network schema , 2009, KDD.

[10]  Chris Sander.  The Journal Bioinformatics, key medium for computational biology , 2002, Bioinform..

[11]  Mohammad Fathian,et al.  Extracting new ideas from the behavior of social network users , 2017 .

[12]  Anna Monreale,et al.  Foundations of Multidimensional Network Analysis , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[13]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[14]  Srinivasan Parthasarathy,et al.  Efficient community detection in large networks using content and links , 2012, WWW.

[15]  Emmanuel Müller,et al.  Focused clustering and outlier detection in large attributed graphs , 2014, KDD.

[16]  Matteo Magnani,et al.  The ML-Model for Multi-layer Social Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[17]  Jiming Liu,et al.  A Multiagent Evolutionary Method for Detecting Communities in Complex Networks , 2016, Comput. Intell..

[18]  Jure Leskovec,et al.  Community Detection in Networks with Node Attributes , 2013, 2013 IEEE 13th International Conference on Data Mining.

[19]  Philip S. Yu,et al.  Integrating meta-path selection with user-guided object clustering in heterogeneous information networks , 2012, KDD.

[20]  Babak Amiri,et al.  A hybrid artificial immune network for detecting communities in complex networks , 2014, Computing.

[21]  David M. Blei,et al.  Relational Topic Models for Document Networks , 2009, AISTATS.

[22]  Clara Pizzuti,et al.  Uncovering communities in multidimensional networks with multiobjective genetic algorithms , 2014, GECCO.

[23]  Hong Cheng,et al.  A model-based approach to attributed graph clustering , 2012, SIGMOD Conference.

[24]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Bin Wu,et al.  Multi-objective community detection in complex networks , 2012, Appl. Soft Comput..

[26]  Olatz Arbelaitz,et al.  An extensive comparative study of cluster validity indices , 2013, Pattern Recognit..

[27]  Inderjit S. Dhillon,et al.  Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.

[28]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[29]  Christophe Ambroise,et al.  Clustering based on random graph model embedding vertex features , 2009, Pattern Recognit. Lett..

[30]  Peter R. Monge,et al.  Theories of Communication Networks , 2003 .

[31]  Mason A. Porter,et al.  Community Detection in Temporal Multilayer Networks, with an Application to Correlation Networks , 2014, Multiscale Model. Simul..

[32]  Haithum Elhadi,et al.  Structure and attributes community detection: comparative analysis of composite, ensemble and selection methods , 2013, SNAKDD '13.

[33]  Emmanuel Viennet,et al.  Community Detection based on Structural and Attribute Similarities , 2012, ICDS 2012.

[34]  Clara Pizzuti,et al.  Evolutionary Clustering for Mining and Tracking Dynamic Multilayer Networks , 2017, Comput. Intell..

[35]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[36]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

[37]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Ling Liu,et al.  Integrating Vertex-centric Clustering with Edge-centric Clustering for Meta Path Graph Analysis , 2015, KDD.

[39]  Daniel Hanisch,et al.  Co-clustering of biological networks and gene expression data , 2002, ISMB.

[40]  Franck Picard,et al.  A mixture model for random graphs , 2008, Stat. Comput..

[41]  Jinxia Liu,et al.  Community Detection Based on Modularity Density and Genetic Algorithm , 2010, 2010 International Conference on Computational Aspects of Social Networks.

[42]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010, Community Detection and Mining in Social Media.

[43]  Hong Cheng,et al.  Clustering Large Attributed Graphs: An Efficient Incremental Approach , 2010, 2010 IEEE International Conference on Data Mining.

[44]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[45]  Yan Liu,et al.  Topic-link LDA: joint models of topic and author community , 2009, ICML '09.

[46]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[47]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[48]  Jean-Jacques Daudin,et al.  Detection of structurally homogeneous subsets in graphs , 2013, Statistics and Computing.

[49]  Ying Wang,et al.  Quantitative Function for Community Detection , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[50]  Gang Liu,et al.  A genetic algorithm for community detection in complex networks , 2013, Journal of Central South University.

[51]  Charu C. Aggarwal,et al.  Relation Strength-Aware Clustering of Heterogeneous Information Networks with Incomplete Attributes , 2012, Proc. VLDB Endow..

[52]  Francesco Calabrese,et al.  ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS , 2013, Data Mining and Knowledge Discovery.

[53]  L. Jiao,et al.  A Novel Clonal Selection Algorithm for Community Detection in Complex Networks , 2015, Comput. Intell..

[54]  Wei Cheng,et al.  Flexible and robust co-regularized multi-domain graph clustering , 2013, KDD.

[55]  Hong Cheng,et al.  Graph Clustering Based on Structural/Attribute Similarities , 2009, Proc. VLDB Endow..