Mining Antagonistic Communities from Social Networks

During social interactions in a community, there are often sub-communities that behave in opposite manner. These antagonistic sub-communities could represent groups of people with opposite tastes, factions within a community distrusting one another, etc. Taking as input a set of interactions within a community, we develop a novel pattern mining approach that extracts for a set of antagonistic sub-communities. In particular, based on a set of user specified thresholds, we extract a set of pairs of sub-communities that behave in opposite ways with one another. To prevent a blow up in these set of pairs, we focus on extracting a compact lossless representation based on the concept of closed patterns. To test the scalability of our approach, we built a synthetic data generator and experimented on the scalability of the algorithm when the size of the dataset and mining parameters are varied. Case studies on an Amazon book rating dataset show the efficiency of our approach and the utility of our technique in extracting interesting information on antagonistic sub-communities.

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

[2]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[3]  Charu C. Aggarwal,et al.  A Tree Projection Algorithm for Generation of Frequent Item Sets , 2001, J. Parallel Distributed Comput..

[4]  M. Kochen,et al.  Contacts and influence , 1978 .

[5]  F. Harary,et al.  STRUCTURAL BALANCE: A GENERALIZATION OF HEIDER'S THEORY1 , 1977 .

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

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

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

[9]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

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

[11]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[12]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[13]  Sanford Labovitz,et al.  A Structural-Behavioral Theory of Intergroup Antagonism , 1975 .

[14]  Jinyan Li,et al.  Maximal Biclique Subgraphs and Closed Pattern Pairs of the Adjacency Matrix: A One-to-One Correspondence and Mining Algorithms , 2007, IEEE Transactions on Knowledge and Data Engineering.

[15]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[16]  Ravi Kanbur,et al.  Community and Class Antagonism , 2007 .

[17]  Jian Pei,et al.  CLOSET+: searching for the best strategies for mining frequent closed itemsets , 2003, KDD '03.

[18]  Jaideep Srivastava,et al.  Predicting trusts among users of online communities: an epinions case study , 2008, EC '08.

[19]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[20]  Jiawei Han,et al.  Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[21]  Indraneel Dasgupta,et al.  'Living' Wage, Class Conflict and Ethnic Strife , 2009, SSRN Electronic Journal.

[22]  V. Traag,et al.  Community detection in networks with positive and negative links. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  F. Harary,et al.  The cohesiveness of blocks in social networks: Node connectivity and conditional density , 2001 .

[24]  Daniel J. Brass,et al.  Social Networks and Perceptions of Intergroup Conflict: The Role of Negative Relationships and Third Parties , 1998 .

[25]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[26]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[27]  M.A.W. Houtsma,et al.  Set-Oriented Mining for Association Rules , 1993, ICDE 1993.

[28]  Jerker Denrell,et al.  Why most people disapprove of me: experience sampling in impression formation. , 2005, Psychological review.

[29]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[30]  John Scott.  Social Network Analysis , 1988 .

[31]  Phillip Bonacich,et al.  Calculating status with negative relations , 2004, Soc. Networks.

[32]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[33]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[34]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[35]  Jennifer Golbeck,et al.  Trust and nuanced profile similarity in online social networks , 2009, TWEB.

[36]  R. Nelson.  THE STRENGTH OF STRONG TIES: SOCIAL NETWORKS AND INTERGROUP CONFLICT IN ORGANIZATIONS , 1989 .

[37]  Ramanathan V. Guha,et al.  Propagation of trust and distrust , 2004, WWW '04.

[38]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[39]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Chao Liu,et al.  Efficient mining of iterative patterns for software specification discovery , 2007, KDD '07.

[41]  Mohammed J. Zaki.  Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[42]  E A Leicht,et al.  Community structure in directed networks. , 2007, Physical review letters.

[43]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[44]  M. Sobel,et al.  Sociological Methodology - 2001 , 2001 .

[45]  F. Harary.  On the notion of balance of a signed graph. , 1953 .

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

[47]  Luís Torgo,et al.  Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings , 2005, PKDD.

[48]  Jiawei Han,et al.  BIDE: efficient mining of frequent closed sequences , 2004, Proceedings. 20th International Conference on Data Engineering.

[49]  A. Evans,et al.  The Power Approach to Intergroup Hostility , 1986 .

[50]  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.

[51]  Robert E. Kraut,et al.  Mopping up: modeling wikipedia promotion decisions , 2008, CSCW.

[52]  Jiawei Han,et al.  Community Mining from Multi-relational Networks , 2005, PKDD.

[53]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[54]  Jochem Tolsma,et al.  Does intergenerational social mobility affect antagonistic attitudes towards ethnic minorities? , 2009, The British journal of sociology.

[55]  Christian Bauckhage,et al.  The slashdot zoo: mining a social network with negative edges , 2009, WWW.

[56]  Xifeng Yan,et al.  CloSpan: Mining Closed Sequential Patterns in Large Datasets , 2003, SDM.