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Community Mining from Signed Social Networks
October 2007 (vol. 19 no. 10)
pp. 1333-1348
Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past, however most of them were designed primarily for networks containing only positive relations and thus not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks so that both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making itself effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set, and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments involving both benchmark and randomly-generated signed networks.

[1] S.H. Strogatz, “Exploring Complex Networks,” Nature, vol. 410, pp. 268-276, 2001.
[2] D.J. Watts and S.H. Strogatz, “Collective Dynamics of Small-World Networks,” Nature, vol. 393, pp. 440-442, 1998.
[3] P. Doreian and A. Mrvar, “A Partitioning Approach to Structural Balance,” Social Networks, vol. 18, no. 2, pp. 149-168, 1996.
[4] K.E. Read, “Cultures of the Central Highlands, New Guinea,” Southwestern J. Anthropology, vol. 10, no. 1, pp. 1-43, 1954.
[5] M. Girvan and M.E.J. Newman, “Community Structure in Social and Biological Networks,” Proc. Nat'l Academy of Science (PNAS), vol. 99, no. 12, pp. 7821-7826, 2002.
[6] N. Bansal, A. Blum, and S. Chawla, “Correlation Clustering,” Proc. 43rd Ann. IEEE Symp. Foundations of Computer Science (FOCS '02), pp. 238-247, 2002.
[7] N. Bansal, A. Blum, and S. Chawla, “Correlation Clustering,” Machine Learning, vol. 56, no. 1-3, pp. 89-113, 2004.
[8] S.D. Kamvar, M.T. Schlosser, and H. Garcia-Molina, “The Eigentrust Algorithm for Reputation Management in P2P Networks,” Proc. 12th Int'l Conf. World Wide Web (WWW '03), pp. 640-651, May 2003.
[9] R. Guha, R. Kumar, P. Raghavan, and A. Tomkins, “Propagation of Trust and Distrust,” Proc. 13th Int'l Conf. World Wide Web (WWW '04), pp. 403-412, 2004.
[10] P. Pons and M. Latapy, “Computing Communities in Large Networks Using Random Walks,” Proc. 20th Int'l Symp. Computer and Information Sciences (ISCIS '05), pp. 284-293, 2005.
[11] G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, “Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society,” Nature, vol. 435, no. 7043, pp. 814-818, 9June 2005.
[12] D.-H. Kim and H. Jeong, “Systematic Analysis of Group Identification in Stock Markets,” Physical Rev. E, vol. 72, 046133, 2005.
[13] M. Fiedler, “Algebraic Connectivity of Graphs,” Czechoslovakian Math. J., vol. 23, no. 98, pp. 298-305, 1973.
[14] A. Pothen, H. Simon, and K.P. Liou, “Partitioning Sparse Matrices with Eigenvectors of Graphs,” SIAM J. Matrix Analysis and Application, vol. 11, no. 3, pp. 430-452, 1990.
[15] M. Fiedler, “A Property of Eigenvectors of Nonnegative Symmetric Matrices and Its Application to Graph Theory,” Czechoslovakian Math. J., vol. 25, no. 100, pp. 619-637, 1975.
[16] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligent, vol. 22, no. 8, pp.888-905, Aug. 2000.
[17] B.W. Kernighan and S. Lin, “An Efficient Heuristic Procedure for Partitioning Graphs,” Bell System Technical, vol. 49, pp. 291-307, 1970.
[18] J. Scott, Social Network Analysis: A Handbook, second ed. Sage Publications, 2000.
[19] R.S. Burt, “Positions in Networks,” Social Forces, vol. 55, no. 1, pp.93-122, 1976.
[20] S. Wasserman and K. Faust, Social Network Analysis. Cambridge Univ. Press, 1994.
[21] J.R. Tyler, D.M. Wilkinson, and B.A. Huberman, “Email as Spectroscopy: Automated Discovery of Community Structure within Organizations,” Proc. First Int'l Conf. Communities and Technologies (C&T '03), pp. 81-96, 2003.
[22] F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, “Defining and Identifying Communities in Networks,” Proc. Nat'l Academy of Science (PNAS), vol. 101, no. 9, pp. 2658-2663, 2004.
[23] G.W. Flake, S. Lawrence, C.L. Giles, and F.M. Coetzee, “Self-Organization and Identification of Web Communities,” Computer, vol. 35, no. 3, pp. 66-71, 2002.
[24] J.M. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” J. ACM, vol. 46, no. 5, pp. 604-632, 1999.
[25] P. Pirolli, J. Pitkow, and R. Rao, “Silk from a Sow's Ear: Extracting Usable Structures from the Web,” Proc. ACM Conf. Human Factors in Computing Systems (CHI), pp. 118-125, 1996.
[26] R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, “Trawling the Web for Emerging Cyber-Communities,” Proc. Eighth Int'l Conf. World Wide Web (WWW '99), pp. 1481-1493, 1999.
[27] S. Chakrabarti, M. van der Berg, and B. Dom, “Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery,” Proc. Eighth Int'l Conf. World Wide Web (WWW '99), pp. 1623-1640, 1999.
[28] G.H. Golub and C.F. Van Loan, Matrix Computations. Johns Hopkins Univ. Press, 1989.
[29] S. Milgram, “The Small World Problem,” Psychology Today, vol. 1, no. 1, pp. 60-67, 1967.
[30] R. Albert, H. Jeong, and A.-L. Barabási, “Diameter of the World Wide Web,” Nature, vol. 401, pp. 130-131, 1999.
[31] M.E.J. Newman, “Fast Algorithm for Detecting Community Structure in Networks,” Physical Rev. E, vol. 69, 066133, 2004.
[32] W.W. Zachary, “An Information Flow Model for Conflict and Fission in Small Groups,” J. Anthropological Research, vol. 33, pp.452-473, 1977.
[33] S. Kropivnik and A. Mrvar, “An Analysis of the Slovene Parliamentary Parties Network,” Developments in Statistics and Methodology, A. Ferligoj, A. Kramberger, eds., pp. 209-216, 1996.
[34] S. Sampson, “Crisis in a Cloister,” PhD dissertation, Cornell Univ., 1969.

Index Terms:
community mining; agent-based approach; random walk; signed social networks
Bo Yang, William Cheung, Jiming Liu, "Community Mining from Signed Social Networks," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 10, pp. 1333-1348, Oct. 2007, doi:10.1109/TKDE.2007.1061
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