Abstract
Subgraph mining algorithms aim at the detection of dense clusters in a graph. In recent years many graph clustering methods have been presented. Most of the algorithms focus on undirected or unweighted graphs. In this work, we propose a novel model to determine the interesting subgraphs also for directed and weighted graphs. We use the method of density computation based on influence functions to identify dense regions in the graph. We present different types of interesting subgraphs. In experiments we show the high clustering quality of our GDens algorithm. GDens outperforms competing approaches in terms of quality and runtime.
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References
Feige, U., Peleg, D., Kortsarz, G.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001)
Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002. LNCS, vol. 2286, pp. 598–612. Springer, Heidelberg (2002)
Liu, G., Wong, L.: Effective pruning techniques for mining quasi-cliques. In: ECML/PKDD, vol. (2), pp. 33–49 (2008)
Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: KDD, pp. 150–160 (2000)
Long, B., Wu, X., Zhang, Z.M., Yu, P.S.: Unsupervised learning on k-partite graphs. In: KDD, pp. 317–326 (2006)
Wu, F., Huberman, B.A.: Finding communities in linear time: A physics approach. CoRR cond-mat/0310600 (2003)
Ruan, J., Zhang, W.: An efficient spectral algorithm for network community discovery and its applications to biological and social networks. In: ICDM, pp. 643–648 (2007)
Long, B., Zhang, Z.M., Yu, P.S.: A probabilistic framework for relational clustering. In: KDD, pp. 470–479 (2007)
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: Scan: a structural clustering algorithm for networks. In: KDD, pp. 824–833 (2007)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE TPAMI 22(8), 888–905 (2000)
Meila, M., Pentney, W.: Clustering by weighted cuts in directed graphs. In: SDM (2007)
Ding, C.H.Q., He, X., Zha, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: ICDM, pp. 107–114 (2001)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 8271–8276 (2002)
Gregory, S.: An algorithm to find overlapping community structure in networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 91–102. Springer, Heidelberg (2007)
Gregory, S.: A fast algorithm to find overlapping communities in networks. In: ECML/PKDD, vol. (1), pp. 408–423 (2008)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 66111 (2004)
Newman, M.E.J.: Modularity and community structure in networks. PNAS USA 103, 8577–8582 (2006)
Chen, J., Zaïane, O.R., Goebel, R.: Detecting communities in social networks using max-min modularity. In: SDM, pp. 978–989 (2009)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases. In: KDD, pp. 226–231 (1996)
Kailing, K., Kriegel, H.P., Kröger, P.: Density-connected subspace clustering for high-dimensional data. In: SDM, pp. 246–257 (2004)
Godsil, C., Royle, G.: Algebraic Graph Theory. Springer, Heidelberg (2001)
Müller, E., Günnemann, S., Assent, I., Seidl, T.: Evaluating clustering in subspace projections of high dimensional data. PVLDB 2(1), 1270–1281 (2009)
Van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)
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Günnemann, S., Seidl, T. (2010). Subgraph Mining on Directed and Weighted Graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_14
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DOI: https://doi.org/10.1007/978-3-642-13672-6_14
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