A Cohesive Subgraph Visualization-Based Approach to Efficiently Discover Large k-Clique Community

Community detection is widely used in many applications such as social network analysis, collaborative filtering and information retrieval. There are many definitions on community of different characteristics and k-Clique community is the one laying its emphasis on the overlapping between communities. We analyze the association between cohesive subgraph visualization (CSV) plot and k-clique community detection. Then, we propose an algorithm named LargeKCliqueCSV to detect large k-clique communities, which reduces search space through CSV plot. At last, we conduct experiments on Stock Market Data datasets. Experimental results show the good scalability of our algorithm comparing with the state-of-art methods Clique Percolation Method and Sequential Clique Percolation algorithm when k grows large.

[1]  Stephen B. Seidman,et al.  Network structure and minimum degree , 1983 .

[2]  A. Nagurney.  Innovations in Financial and Economic Networks , 2003 .

[3]  Janez Konc,et al.  An improved branch and bound algorithm for the maximum clique problem , 2007 .

[4]  Anthony K. H. Tung,et al.  CSV: visualizing and mining cohesive subgraphs , 2008, SIGMOD Conference.

[5]  Jianyong Wang,et al.  CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[6]  C. Lee Giles,et al.  Efficient identification of Web communities , 2000, KDD '00.

[7]  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.

[8]  Pan Hui,et al.  Distributed community detection in delay tolerant networks , 2007, MobiArch '07.

[9]  Steve Gregory,et al.  An Algorithm to Find Overlapping Community Structure in Networks , 2007, PKDD.

[10]  Neil J. Hurley,et al.  Detecting Highly Overlapping Communities with Model-Based Overlapping Seed Expansion , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[11]  Srinivasan Parthasarathy,et al.  An ensemble framework for clustering protein-protein interaction networks , 2007, ISMB/ECCB.

[12]  Malik Magdon-Ismail,et al.  Finding communities by clustering a graph into overlapping subgraphs , 2005, IADIS AC.

[13]  S. Wasserman,et al.  Models and methods in social network analysis , 2005 .

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

[15]  Ulrik Brandes,et al.  Network Analysis: Methodological Foundations , 2010 .

[16]  Sampo Niskanen,et al.  Cliquer user's guide, version 1.0 , 2003 .

[17]  T. Vicsek,et al.  Clique percolation in random networks. , 2005, Physical review letters.

[18]  Jiawei Han,et al.  Mining coherent dense subgraphs across massive biological networks for functional discovery , 2005, ISMB.

[19]  Malik Magdon-Ismail,et al.  Efficient Identification of Overlapping Communities , 2005, ISI.

[20]  Joost N. Kok,et al.  Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings , 2007, PKDD.

[21]  Illés J. Farkas,et al.  CFinder: locating cliques and overlapping modules in biological networks , 2006, Bioinform..

[22]  Li Taiyong,et al.  CCDCD:Community Core Mining with Dynamic Constrains Based on Graph Density , 2009 .

[23]  Changjie Tang,et al.  An MDL approach to efficiently discover communities in bipartite network , 2010, Journal of Central South University.

[24]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

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

[26]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[27]  Robert E. Tarjan,et al.  Graph Clustering and Minimum Cut Trees , 2004, Internet Math..

[28]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[29]  Ulrik Brandes,et al.  Network Analysis: Methodological Foundations (Lecture Notes in Computer Science) , 2005 .

[30]  R. Cotterrell,et al.  The Sociological Concept of Law , 1983 .

[31]  J. Kumpula,et al.  Sequential algorithm for fast clique percolation. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[33]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[34]  Pan Hui,et al.  Visualizing community detection in opportunistic networks , 2007, CHANTS '07.

[35]  Philip S. Yu,et al.  Discovering Overlapping Communities of Named Entities , 2006, PKDD.

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

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

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

[39]  Linton C. Freeman,et al.  The Sociological Concept of "Group": An Empirical Test of Two Models , 1992, American Journal of Sociology.