Multiscale Local Community Detection in Social Networks

In real-world social networks, global information (e.g., the number of nodes and the connections between them) is incomplete or expensive to acquire; therefore, local community detection becomes especially important. Local community detection is used to identify the local community to which the given starting node belongs according to local information. For a given node, most existing local community detection methods can only find single scale local communities but not those of variable sizes. However, local communities with different scales are often required. Therefore, it is necessary and meaningful to find local communities of the given starting node with different scales; we call this multiscale local community detection. In this paper, we propose a new local modularity inspired by the global modularity and prove the equivalence of the proposed local modularity with two other typical local modularities. Furthermore, to detect local communities with different scales, we present a method based on the proposed local modularity. We test this method on several synthetic and real datasets, and the experimental results indicate that the detected community is meaningful and its scale can be changed reasonably.

[1]  Alex Arenas,et al.  Analysis of the structure of complex networks at different resolution levels , 2007, physics/0703218.

[2]  Chonghui Guo,et al.  Evolutionary community structure discovery in dynamic weighted networks , 2014 .

[3]  F. Rao,et al.  Local modularity measure for network clusterizations. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Xianchao Zhang,et al.  Extracting Local Community Structure from Local Cores , 2011, DASFAA Workshops.

[5]  J. Lawton.  Structure of food webs , 1978, Nature.

[6]  R. Guimerà,et al.  Modularity from fluctuations in random graphs and complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Marco Pellegrini,et al.  Extraction and classification of dense communities in the web , 2007, WWW '07.

[9]  Alexander Rives,et al.  Modular organization of cellular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Jing Li,et al.  Robust Local Community Detection: On Free Rider Effect and Its Elimination , 2015, Proc. VLDB Endow..

[11]  Andrea Lancichinetti,et al.  Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Qiong Chen,et al.  A method for local community detection by finding maximal-degree nodes , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[13]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[14]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

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

[16]  James P. Bagrow.  Evaluating local community methods in networks , 2007, 0706.3880.

[17]  D. Mason,et al.  Compartments revealed in food-web structure , 2003, Nature.

[18]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[20]  A. Clauset.  Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Jingchun Chen,et al.  Detecting functional modules in the yeast protein-protein interaction network , 2006, Bioinform..

[22]  Feng Luo,et al.  Exploring Local Community Structures in Large Networks , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[23]  Osmar R. Zaïane,et al.  Top Leaders Community Detection Approach in Information Networks , 2010 .

[24]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[25]  Yiannis Kompatsiaris,et al.  Community detection in Social Media , 2012, Data Mining and Knowledge Discovery.

[26]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[27]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Yiannis Kompatsiaris,et al.  Bridge Bounding: A Local Approach for Efficient Community Discovery in Complex Networks , 2009, 0902.0871.

[30]  Michel Crampes,et al.  Survey on Social Community Detection , 2013, Social Media Retrieval.

[31]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Masaru Kitsuregawa,et al.  A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering , 2002, DNIS.

[33]  Dino Pedreschi,et al.  A classification for community discovery methods in complex networks , 2011, Stat. Anal. Data Min..

[34]  Boleslaw K. Szymanski,et al.  Towards Linear Time Overlapping Community Detection in Social Networks , 2012, PAKDD.

[35]  Maurice Tchuente,et al.  Local Community Identification in Social Networks , 2012, Parallel Process. Lett..

[36]  Hao Huang,et al.  GMAC: A Seed-Insensitive Approach to Local Community Detection , 2013, DaWaK.

[37]  Jure Leskovec,et al.  Empirical comparison of algorithms for network community detection , 2010, WWW '10.

[38]  Steve Gregory,et al.  Finding missing edges and communities in incomplete networks , 2011, ArXiv.

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

[40]  David Lusseau,et al.  The emergent properties of a dolphin social network , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[41]  David F. Gleich,et al.  Heat kernel based community detection , 2014, KDD.

[42]  Inderjit S. Dhillon,et al.  Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion , 2015, IEEE Transactions on Knowledge and Data Engineering.

[43]  Zhifeng Hao,et al.  Local Community Detection Using Link Similarity , 2012, Journal of Computer Science and Technology.

[44]  Erik M Bollt,et al.  Local method for detecting communities. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  Jure Leskovec,et al.  Statistical properties of community structure in large social and information networks , 2008, WWW.

[46]  Fan Chung Graham,et al.  Local Graph Partitioning using PageRank Vectors , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[47]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Qiong Chen,et al.  Detecting local community structures in complex networks based on local degree central nodes , 2013 .

[49]  Cun-Quan Zhang,et al.  Optimal local community detection in social networks based on density drop of subgraphs , 2014, Pattern Recognit. Lett..

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

[51]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[52]  Pascal Pons,et al.  Post-processing hierarchical community structures: Quality improvements and multi-scale view , 2006, Theor. Comput. Sci..