Abstract
We introduce the problem of identifying representative nodes in probabilistic graphs, motivated by the need to produce different simple views to large BisoNets. We define a probabilistic similarity measure for nodes, and then apply clustering methods to find groups of nodes. Finally, a representative is output from each cluster. We report on experiments with real biomedical data, using both the k-medoids and hierarchical clustering methods in the clustering step. The results suggest that the clustering based approaches are capable of finding a representative set of nodes.
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Kötter, T., Berthold, M.R.: From Information Networks to Bisociative Information Networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 33–50. Springer, Heidelberg (2012)
Sevon, P., Eronen, L., Hintsanen, P., Kulovesi, K., Toivonen, H.: Link Discovery in Graphs Derived from Biological Databases. In: Leser, U., Naumann, F., Eckman, B. (eds.) DILS 2006. LNCS (LNBI), vol. 4075, pp. 35–49. Springer, Heidelberg (2006)
Yan, D., Huang, L., Jordan, M.I.: Fast approximate spectral clustering. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 907–916. ACM, New York (2009)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley Inc., New York (1990)
Ester, M., Kriegel, H.P., Xu, X.: Knowledge discovery in large spatial databases: focusing techniques for efficient class identification. In: 4th International Symposium on Advances in Spatial Databases (SDD 1995), pp. 67–82. Springer, London (1995)
Riquelme, J.C., Aguilar-Ruiz, J.S., Toro, M.: Finding representative patterns with ordered projections. Pattern Recognition 36(4), 1009–1018 (2003)
Rozsypal, A., Kubat, M.: Selecting representative examples and attributes by a genetic algorithm. Intelligent Data Analysis 7(4), 291–304 (2003)
Pan, F., Wang, W., Tung, A.K.H., Yang, J.: Finding representative set from massive data. In: The 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 338–345. IEEE Computer Society, Washington, DC (2005)
DeLucia, D., Obraczka, K.: Multicast feedback suppression using representatives. In: 16th Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution (INFOCOM 1997), pp. 463–470. IEEE Computer Society, Washington, DC (1997)
Liang, C., Hock, N.C., Liren, Z.: Selection of representatives for feedback suppression in reliable multicast protocols. Electronics Letters 37(1), 23–25 (2001)
Domingos, P., Richardson, M.: Mining the network value of customers. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), pp. 57–66. ACM, New York (2001)
Tong, H., Faloutsos, C.: Center-piece subgraphs: problem definition and fast solutions. In: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pp. 404–413. ACM, New York (2006)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1999)
Colbourn, C.J.: The Combinatiorics of Network Reliability. Oxford University Press (1987)
Valiant, L.: The complexity of enumeration and reliability problems. SIAM Journal on Computing 8, 410–421 (1979)
Hintsanen, P., Toivonen, H.: Finding reliable subgraphs from large probabilistic graphs. Data Mining and Knowledge Discovery 17(1), 3–23 (2008)
Han, J., Kamber, M.: Data Mining. Concepts and Techniques, 2nd edn. Morgan Kaufmann (2006)
Köhler, S., Bauer, S., Horn, D., Robinson, P.: Walking the interactome for prioritization of candidate disease genes. American Journal of Human Genetics 82(4), 949–958 (2008)
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Langohr, L., Toivonen, H. (2012). Finding Representative Nodes in Probabilistic Graphs. In: Berthold, M.R. (eds) Bisociative Knowledge Discovery. Lecture Notes in Computer Science(), vol 7250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31830-6_15
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DOI: https://doi.org/10.1007/978-3-642-31830-6_15
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