Abstract
Most of the works on learning from networked data assume that the network is static. In this paper we consider a different scenario, where the network is dynamic, i.e. nodes/relationships can be added or removed and relationships can change in their type over time. We assume that the “core” of the network is more stable than the “marginal” part of the network, nevertheless it can change with time. These changes are of interest for this work, since they reflect a crucial step in the network evolution. Indeed, we tackle the problem of discovering evolution chains, which express the temporal evolution of the “core” of the network. To describe the “core” of the network, we follow a frequent pattern-mining approach, with the critical difference that the frequency of a pattern is computed along a time-period and not on a static dataset. The proposed method proceeds in two steps: 1) identification of changes through the discovery of emerging patterns; 2) composition of evolution chains by joining emerging patterns. We test the effectiveness of the method on both real and synthetic data.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB, pp. 487–499. Morgan Kaufmann (1994)
Berlingerio, M., Bonchi, F., Bringmann, B., Gionis, A.: Mining graph evolution rules. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part I. LNCS, vol. 5781, pp. 115–130. Springer, Heidelberg (2009)
Ceci, M., Appice, A., Loglisci, C., Caruso, C., Fumarola, F., Malerba, D.: Novelty detection from evolving complex data streams with time windows. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 563–572. Springer, Heidelberg (2009)
Ceci, M., Appice, A., Malerba, D.: Discovering emerging patterns in spatial databases: A multi-relational approach. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 390–397. Springer, Heidelberg (2007)
Di Mauro, N., Malerba, D.: Mining networked data. In: Chawla, N., King, I., Sperduti, A. (eds.) Symposium on Computational Intelligence and Data Mining, IEEE-CIDM 2011, p. xx. IEEE (2011)
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: KDD, pp. 43–52 (1999)
Malerba, D.: A relational perspective on spatial data mining. IJDMMM 1(1), 103–118 (2008)
Sun, J., Faloutsos, C., Papadimitriou, S., Yu, P.S.: Graphscope: parameter-free mining of large time-evolving graphs. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 687–696. ACM, New York (2007)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)
Zhu, J., Xie, Q., Chin, E.J.: A hybrid time-series link prediction framework for large social network. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012, Part II. LNCS, vol. 7447, pp. 345–359. Springer, Heidelberg (2012)
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Loglisci, C., Ceci, M., Malerba, D. (2013). Discovering Evolution Chains in Dynamic Networks. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2012. Lecture Notes in Computer Science(), vol 7765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37382-4_13
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DOI: https://doi.org/10.1007/978-3-642-37382-4_13
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