Global Community Extraction in Social Network Analysis

Great efforts have been made in retrieving the structure of social networks, in which one of the most relevant features is community extraction. A community in social networks presents a group of people focusing on a common topic or interest. Extracting all communities in the whole network, one can easily classify and analyze a specified group of people, which yields amazing results. Global community extraction is due to this demand. In global community extraction (also global clustering), each person of the input network is assigned to a community in the output of the method. This chapter focuses on global community extraction in social network analysis, previous methods proposed by some outstanding researchers, future directions, and so on. DOI: 10.4018/978-1-4666-2806-9.ch010

[1]  Chris H. Q. Ding,et al.  Web document clustering using hyperlink structures , 2001, Comput. Stat. Data Anal..

[2]  Ravi Kumar,et al.  Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.

[3]  Xianchao Zhang,et al.  C&C: An Effective Algorithm for Extracting Web Community Cores , 2010, DASFAA Workshops.

[4]  Kazuhisa Makino,et al.  New Algorithms for Enumerating All Maximal Cliques , 2004, SWAT.

[5]  Alessandro Panconesi,et al.  Analysis and Experimental Evaluation of a Simple Algorithm for Collaborative Filtering in Planted Partition Models: Extended Abstract , 2003, FSTTCS.

[6]  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.

[7]  Aditi Sharan,et al.  Identification of Web Communities through Link Based Approaches , 2009, 2009 International Conference on Information Management and Engineering.

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

[9]  Edwin R. Hancock,et al.  Graph matching and clustering using spectral partitions , 2006, Pattern Recognit..

[10]  Brian W. Kernighan,et al.  An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..

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

[12]  Leon Danon,et al.  The effect of size heterogeneity on community identification in complex networks , 2006, physics/0601144.

[13]  A. Hoffman,et al.  Lower bounds for the partitioning of graphs , 1973 .

[14]  Jennifer Xu,et al.  Identifying Cohesive Local Community Structures in Networks , 2009, ICIS.

[15]  Maria Manuela Cruz-Cunha,et al.  Handbook of Research on Business Social Networking: Organizational, Managerial, and Technological Dimensions , 2011 .

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

[17]  M. Fiedler.  Algebraic connectivity of graphs , 1973 .

[18]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

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

[20]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[21]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Padhraic Smyth,et al.  A Spectral Clustering Approach To Finding Communities in Graph , 2005, SDM.

[23]  Geoffrey Zweig,et al.  Syntactic Clustering of the Web , 1997, Comput. Networks.

[24]  G. Kedem,et al.  An algorithm for quadrisection and its application to standard cell placement , 1988 .

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

[26]  V. Carchiolo,et al.  Extending the definition of modularity to directed graphs with overlapping communities , 2008, 0801.1647.

[27]  Masaru Kitsuregawa,et al.  An approach to relate the Web communities through bipartite graphs , 2001, Proceedings of the Second International Conference on Web Information Systems Engineering.

[28]  Leonid Zhukov,et al.  Clustering of bipartite advertiser-keyword graph , 2003 .

[29]  Jon M. Kleinberg,et al.  Inferring Web communities from link topology , 1998, HYPERTEXT '98.

[30]  Xianchao Zhang,et al.  Extracting Local Web Communities Using Lexical Similarity , 2010, DASFAA Workshops.

[31]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[32]  Ron Shamir,et al.  A clustering algorithm based on graph connectivity , 2000, Inf. Process. Lett..

[33]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[34]  Stephen Guattery,et al.  On the Quality of Spectral Separators , 1998, SIAM J. Matrix Anal. Appl..

[35]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[36]  Christos Gkantsidis,et al.  Conductance and congestion in power law graphs , 2003, SIGMETRICS '03.

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

[38]  Satu Elisa Schaeffer,et al.  Stochastic Local Clustering for Massive Graphs , 2005, PAKDD.

[39]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[40]  S. Dongen.  Graph clustering by flow simulation , 2000 .

[41]  Sandra Sudarsky,et al.  Massive Quasi-Clique Detection , 2002, LATIN.

[42]  Ravi Kumar,et al.  Discovering Large Dense Subgraphs in Massive Graphs , 2005, VLDB.

[43]  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.

[44]  Pekka Orponen,et al.  Local Clustering of Large Graphs by Approximate Fiedler Vectors , 2005, WEA.

[45]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  Shang-Hua Teng,et al.  Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems , 2003, STOC '04.

[47]  Satish Rao,et al.  Graph partitioning using single commodity flows , 2009, JACM.

[48]  Pekka Orponen,et al.  Locally computable approximations for spectral clustering and absorption times of random walks , 2008, ArXiv.

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

[50]  Jirí Síma,et al.  On the NP-Completeness of Some Graph Cluster Measures , 2005, SOFSEM.

[51]  Randy Goebel,et al.  Local Community Identification in Social Networks , 2009, 2009 International Conference on Advances in Social Network Analysis and Mining.

[52]  Robert E. Tarjan,et al.  A Fast Parametric Maximum Flow Algorithm and Applications , 1989, SIAM J. Comput..

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

[54]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956 .

[55]  Satish Rao,et al.  A Flow-Based Method for Improving the Expansion or Conductance of Graph Cuts , 2004, IPCO.

[56]  Ulrik Brandes,et al.  Experiments on Graph Clustering Algorithms , 2003, ESA.

[57]  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).

[58]  C. Lee Giles,et al.  Self-Organization and Identification of Web Communities , 2002, Computer.

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

[60]  Satu Elisa Schaeffer,et al.  Graph Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[61]  Kevin J. Lang,et al.  An algorithm for improving graph partitions , 2008, SODA '08.

[62]  Guimei Liu,et al.  Effective Pruning Techniques for Mining Quasi-Cliques , 2008, ECML/PKDD.

[63]  Santosh S. Vempala,et al.  On clusterings: Good, bad and spectral , 2004, JACM.