Distance-Based Community Search (Invited Talk Extended Abstract)

  • Francesco BonchiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11376)


Suppose we have identified a set of subjects in a terrorist network suspected of organizing an attack. Which other subjects, likely to be involved, should we keep under control? Similarly, given a set of patients infected with a viral disease, which other people should we monitor? Given a set of companies trading anomalously on the stock market: is there any connection among them that could explain the anomaly? Given a set of proteins of interest, which other proteins participate in pathways with them? Given a set of users in a social network that clicked an ad, to which other users (by the principle of “homophily”) should the same ad be shown?



I wish to thank all the co-authors of the various papers on which this invited talk is built: Natali Ruchansky, Ioanna Tsalouchidou, David García-Soriano, Francesco Gullo, Nicolas Kourtellis, Ricardo Baeza-Yates.


  1. 1.
    Akoglu, L., et al.: Mining connection pathways for marked nodes in large graphs. In: SDM (2013)CrossRefGoogle Scholar
  2. 2.
    Andersen, R., Lang, K.J.: Communities from seed sets. In: WWW (2006)Google Scholar
  3. 3.
    Barbieri, N., Bonchi, F., Galimberti, E., Gullo, F.: Efficient and effective community search. DAMI 29(5), 1406–1433 (2015)MathSciNetGoogle Scholar
  4. 4.
    Bavelas, A.: A mathematical model of group structure. Hum. Organ. 7, 16–30 (1948)CrossRefGoogle Scholar
  5. 5.
    Burt, R.: Structural Holes: The Social Structure of Competition. Harvard University Press (1992)Google Scholar
  6. 6.
    Cui, W., Xiao, Y., Wang, H., Wang, W.: Local search of communities in large graphs. In: SIGMOD (2014)Google Scholar
  7. 7.
    Faloutsos, C., McCurley, K.S., Tomkins, A.: Fast discovery of connection subgraphs. In: KDD (2004)Google Scholar
  8. 8.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD (2003)Google Scholar
  10. 10.
    Kloumann, I.M., Kleinberg, J.M.: Community membership identification from small seed sets. In: KDD (2014)Google Scholar
  11. 11.
    Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87(19), 198701 (2001)CrossRefGoogle Scholar
  13. 13.
    Marchiori, M., Latora, V.: Harmony in the small-world. Phys. A: Stat. Mech. Appl. 285(3–4), 539–546 (2000)CrossRefGoogle Scholar
  14. 14.
    Przytycka, T., Singh, M., Slonim, D.: Toward the dynamic interactome: it’s about time. Brief. Bioinform. 11(1), 15–29 (2010). Scholar
  15. 15.
    Ruchansky, N., Bonchi, F., García-Soriano, D., Gullo, F., Kourtellis, N.: The minimum wiener connector problem. In: SIGMOD (2015)Google Scholar
  16. 16.
    Ruchansky, N., Bonchi, F., García-Soriano, D., Gullo, F., Kourtellis, N.: To be connected, or not to be connected: that is the minimum inefficiency subgraph problem. In: CIKM (2017)Google Scholar
  17. 17.
    Sozio, M., Gionis, A.: The community-search problem and how to plan a successful cocktail party. In: KDD (2010)Google Scholar
  18. 18.
    Tong, H., Faloutsos, C.: Center-piece subgraphs: problem definition and fast solutions. In: KDD, pp. 404–413 (2006)Google Scholar
  19. 19.
    Wiener, H.: Structural determination of paraffin boiling points. J. Am. Chem. Soc. 69(1), 17–20 (1947)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ISI FoundationTurinItaly
  2. 2.EurecatBarcelonaSpain

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