Abstract
The hierarchical clustering methods based on vertex similarity can be employed for community discovery. Vertex similarity metric is the most important part of these methods. However, the existing metrics do not perform well compared with the state-of-the-art algorithms. In this paper, we propose a new vertex similarity metric based on distance neighbor model, called Distance Neighbor Ratio Metric (DNRM), for community discovery. DNRM considers both distance and nearby edge density which are essential measures in community structure. Compared with the existing metrics of vertex similarity, DNRM outperforms substantially in community discovery quality and the computing time. The experiments are designed rigorously involving both well-known social networks in real world and computer generated networks.
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References
Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)
Price, D.: Networks of scientific papers. In: Kochen, M. (ed.) The Growth of Knowledge: Readings on Organization and Retrieval of Information, pp. 145–155. Wiley, Chichester (1965)
Dunne, J.A., Williams, R.J., Martinez, N.D.: Foodweb structure and network theory: The role of connectance and size. Proc. Natl. Acad. Sci. USA 99, 12917–12922 (2002)
Kauffman, S.A.: Metabolic stability and epigenesis in randomly connected nets. J. Theor. Bio. 22, 437–467 (1969)
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., Sakaki, Y.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA 98, 4569–4574 (2001)
Sales-Pardo, M., Guimera, R., Moreira, A.A., Amaral, L.A.N.: Module identification in bipartite and directed networks. Proc. Natl. Acad. Sci. USA 104, 15224–15229 (2007)
Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal 49, 291–308 (1970)
Fiedler, M.: Algebraic connectivity of graphs. Czech. Math. J. 23, 298–305 (1973)
Pothen, A., Simon, H., Liou, K.P.: Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11, 430–452 (1990)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)
Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as spectroscopy: automated discovery of community structure within organizations. In: Huysman, M.H., Wenger, E., Wulf, V. (eds.) Proceedings of the International Conference on Communities and Technologies, pp. 81–96. Springer, Heidelberg (2003)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and indentifying communities in networks. Proc. Nat. Academy of Science (PNAS) 101(9), 2658–2663 (2004)
Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self-Organization and Identification of Web Communities. Computer 35(3), 66–71 (2002)
Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. J. ACM 46(5), 604–632 (1999)
Pirolli, P., Pitkow, J., Rao, R.: Silk from a Sows Ear:Extracting Usable Structures from the Web. In: Proc. ACM Conf. Human Factors in Computing Systems (CHI), pp. 118–125 (1996)
Donetti, L., Münoz, M.A.: Detecting network communities: a new systematic and powerful algorithm. J. Stat. Mech., P10012 (2004)
Capocci, A., Servedio, V.D.P., Caldarelli, G., Colaiori, F.: The scale-free topology of market investments. Physica A 352, 669 (2005)
Alves, N.A.: Unveiling community structures in weighted networks. Phys. Rev. E 76(3), 36101 (2007)
Reichardt, J., Bornholdt, S.: Detecting fuzzy community structures in complex networks with a potts model. Phys. Rev. Lett. 93(21), 218701 (2004)
Zhou, H.: Distance, dissimilarity index, and network community structure. Phys. Rev. E 67(6), 061901 (2003)
Arenas, A., Diaz-Guilera, A., Peerez-Vicente, C.J.: Synchronization reveals topological scalses in complex networks. Phys. Rev. Lett. 96(11), 114102 (2006)
Fortunato, S.: Community detection in graphs, arXiv, 0906.0612 (2009)
Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38, 321–330 (2004)
Jaccard, P.: Etude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Socit Vaudoise des Sciences Naturelles 37, 547–579 (1901)
Barnes, E.R.: An algorithm for partitioning the nodes of a graph. SIAM Journal for Algorithms and Discrete Methods 3, 541–550 (1982)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabsi, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)
Gleiser, P., Danon, L.: Community structure in Jazz. Adv. Complex Systems 6, 565–573 (2003)
Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin commu-nity of doubful sound features a large problem of long-lasting associations. Behav. Ecol. Sociobiol. 54, 396–405 (2003)
Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72, 026132 (2005)
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Li, Y. (2011). A New Vertex Similarity Metric for Community Discovery: A Distance Neighbor Model. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6591. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20039-7_23
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DOI: https://doi.org/10.1007/978-3-642-20039-7_23
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