Abstract
Many applications see huge demands for discovering relevant patterns in dynamic attributed graphs, for instance in the context of social interaction analysis. It is often possible to associate a hierarchy on the attributes related to graph vertices to explicit prior knowledge. For example, considering the study of scientific collaboration networks, conference venues and journals can be grouped with respect to types or topics. We propose to extend a recent constraint-based mining method by exploiting such hierarchies on attributes. We define an algorithm that enumerates all multi-level co-evolution sub-graphs, i.e., induced sub-graphs that satisfy a topologic constraint and whose vertices follow the same evolution on a set of attributes during some timestamps. Experiments show that hierarchies make it possible to return more concise collections of patterns without information loss in a feasible time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., Pedreschi, D.: As time goes by: Discovering eras in evolving social networks. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part I. LNCS, vol. 6118, pp. 81–90. Springer, Heidelberg (2010)
Boden, B., Günnemann, S., Seidl, T.: Tracing clusters in evolving graphs with node attributes. In: CIKM, pp. 2331–2334 (2012)
Borgwardt, K.M., Kriegel, H.P., Wackersreuther, P.: Pattern mining in frequent dynamic subgraphs. In: Int. Conf. on Data Mining (ICDM), pp. 818–822 (2006)
Bringmann, B., Nijssen, S.: What is frequent in a single graph? In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 858–863. Springer, Heidelberg (2008)
Cakmak, A., Özsoyoglu, G.: Taxonomy-superimposed graph mining. In: EDBT, pp. 217–228 (2008)
Calders, T., Ramon, J., van Dyck, D.: Anti-monotonic overlap-graph support measures. In: ICDM, pp. 73–82 (2008)
Cerf, L., Besson, J., Robardet, C., Boulicaut, J.-F.: Closed patterns meet n-ary relations. TKDD 3(1), 3:1–3:36 (2009)
Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Comput. Survey 38(1) (2006)
Desmier, E., Plantevit, M., Robardet, C., Boulicaut, J.-F.: Cohesive co-evolution patterns in dynamic attributed graphs. In: Discovery Science, pp. 110–124 (2012)
Desmier, E., Plantevit, M., Robardet, C., Boulicaut, J.-F.: Trend mining in dynamic attributed graphs. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part I. LNCS, vol. 8188, pp. 654–669. Springer, Heidelberg (2013)
Ester, M., Ge, R., Gao, B.J., Hu, Z., Ben-moshe, B.: Joint cluster analysis of attribute data and relationship data. In: SIAM SDM, pp. 246–257 (2006)
Inokuchi, A.: Mining generalized substructures from a set of labeled graphs. In: ICDM, pp. 415–418 (2004)
Jin, R., McCallen, S., Almaas, E.: Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks. In: ICDM, pp. 541–546. IEEE (2007)
Moser, F., Colak, R., Rafiey, A., Ester, M.: Mining cohesive patterns from graphs with feature vectors. In: SDM, pp. 593–604 (2009)
Mougel, P.N., Rigotti, C., Plantevit, M., Gandrillon, O.: Finding maximal homogeneous clique sets. Knowl. Inf. Syst. 39(3), 579–608 (2014)
Nijssen, S., Kok, J.N.: Frequent graph mining and its application to molecular databases. In: Systems, Man and Cybernetics (SMC), vol. 5, pp. 4571–4577 (2004)
Prado, A., Plantevit, M., Robardet, C., Boulicaut, J.F.: Mining graph topological patterns. IEEE TKDE, 1–14 (2013)
Robardet, C.: Constraint-based pattern mining in dynamic graphs. In: ICDM, pp. 950–955 (2009)
Silva, A., Meira Jr., W., Zaki, M.J.: Mining attribute-structure correlated patterns in large attributed graphs. PVLDB 5(5), 466–477 (2012)
Wu, Y., Yang, S., Yan, X.: Ontology-based subgraph querying. In: ICDE, pp. 697–708 (2013)
Yan, X., Han, J.: gSpan: Graph-Based Substructure Pattern Mining. In: ICDM, pp. 721–724 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Desmier, É., Plantevit, M., Robardet, C., Boulicaut, JF. (2014). Granularity of Co-evolution Patterns in Dynamic Attributed Graphs. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-12571-8_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12570-1
Online ISBN: 978-3-319-12571-8
eBook Packages: Computer ScienceComputer Science (R0)