Algorithms for Subnetwork Mining in Heterogeneous Networks

  • Guillaume Fertin
  • Hafedh Mohamed Babou
  • Irena Rusu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7276)

Abstract

Subnetwork mining is an essential issue in network analysis, with specific applications e.g. in biological networks, social networks, information networks and communication networks. Recent applications require the extraction of subnetworks (or patterns) involving several relations between the objects of interest, each such relation being given as a network. The complexity of a particular mining problem increases with the different nature of the networks, their number, their size, the topology of the requested pattern, the criteria to optimize. In this emerging field, our paper deals with two networks respectively represented as a directed acyclic graph and an undirected graph, on the same vertex set. The sought pattern is a longest path in the directed graph whose vertex set induces a connected subgraph in the undirected graph. This problem has immediate applications in biological networks, and predictable applications in social, information and communication networks. We study the complexity of the problem, thus identifying polynomial, NP-complete and APX-hard cases. In order to solve the difficult cases, we propose a heuristic and a branch-and-bound algorithm. We further perform experimental evaluation on both simulated and real data.

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References

  1. 1.
    Barabási, A.-L.: Scale-free networks: a decade and beyond. Science 325, 412–413 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Blin, G., Fertin, G., Mohamed-Babou, H., Rusu, I., Sikora, F., Vialette, S.: Algorithmic Aspects of Heterogeneous Biological Networks Comparison. In: Wang, W., Zhu, X., Du, D.-Z. (eds.) COCOA 2011. LNCS, vol. 6831, pp. 272–286. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Boyer, F., Morgat, A., Labarre, L., Pothier, J., Viari, A.: Syntons, metabolons and interactons: an exact graph-theoretical approach for exploring neighbourhood between genomic and functional data. Bioinformatics 21(23), 4209–4215 (2005)CrossRefGoogle Scholar
  4. 4.
    Bunke, H.: Graph matching: theoretical foundations, algorithms and applications. In: Proc. Vision Interface, pp. 82–88 (2000)Google Scholar
  5. 5.
    Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Mining hidden community in heterogeneous social networks. In: Proc. of the 3rd International Workshop on Link Discovery, pp. 58–65 (2005)Google Scholar
  6. 6.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence 18, 265–298 (2004)CrossRefGoogle Scholar
  7. 7.
    Durek, P., Walther, D.: The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles. BMC Systems Biology 2(1) (2008)Google Scholar
  8. 8.
    Dzeroski, S., Lavrac, N.: Relational data mining. Springer (2001)Google Scholar
  9. 9.
    Erdös, P., Rényi, A.: On random graphs, I. Publicationes Mathematicae (Debrecen) 6, 290–297 (1959)MathSciNetMATHGoogle Scholar
  10. 10.
    Gai, A.-T., Habib, M., Paul, C., Raffinot, M.: Identifying common connected components of graphs. Technical Report RR-LIRMM-03016, LIRMM (2003)Google Scholar
  11. 11.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: a guide to the theory of NP-completeness. W.H. Freeman (1979)Google Scholar
  12. 12.
    Kelley, B.P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B.R., Ideker, T.: Pathblast: a tool for alignment of protein interaction networks. Nucleic Acids Research 32, 83–88 (2004)CrossRefGoogle Scholar
  13. 13.
    Matsuo, Y., Hamasaki, M., Takeda, H., Mori, J., Bollegara, D., Nakamura, Y., Nishimura, T., Hasida, K., Ishizuka, M.: Spinning multiple social networks for semantic web. In: Proc. of the Twenty-First National Conference on Artificial Intelligence (2006)Google Scholar
  14. 14.
    Sharan, R., Suthram, S., Kelley, R.M., Kuhn, T., Mccuine, S., Uetz, P., Sittler, T., Karp, R.M., Ideker, T.: Conserved patterns of protein interaction in multiple species. National Academy of Sciences 102(6), 1974–1979 (2005)CrossRefGoogle Scholar
  15. 15.
    Vicentini, R., Menossi, M.: Data mining and knowledge discovery in real life applications. Julio Ponce and Adem Karahoca edition. In-tech (2009)Google Scholar
  16. 16.
    Wernicke, S., Rasche, F.: Simple and fast alignment of metabolic pathways by exploiting local diversity. Bioinformatics 23, 1978–1985 (2007)CrossRefGoogle Scholar
  17. 17.
    Williams, E., Bowles, D.J.: Coexpression of neighboring genes in the genome of arabidopsis thaliana. Genome Research 14, 1060–1067 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guillaume Fertin
    • 1
  • Hafedh Mohamed Babou
    • 1
  • Irena Rusu
    • 1
  1. 1.LINA, UMR 6241Université de NantesFrance

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