Learning Pretopological Spaces to Extract Ego-Centered Communities

  • Gaëtan CaillautEmail author
  • Guillaume Cleuziou
  • Nicolas Dugué
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


We present a pretopological based approach to extract ego-centered communities. Classical methods often consider only one structural feature of the network, whereas pretopology enables to do multi-criteria analysis. Our approach consists in learning a logical combination of network’s descriptors to define a pretopological space. Ego-centered communities are extracted by computing the elementary closure of each node. The quality of such communities is evaluated against the ground truth communities. We show the benefits of our method by comparing it to others on both real and synthetic networks.


Community extraction Pretopology Ego-centered communities 


  1. 1.
    Belmandt, Z.: Basics of Pretopology. Hermann, Paris (2011)Google Scholar
  2. 2.
    Caillaut, G., Cleuziou, G.: Learning pretopological spaces to model complex propagation phenomena: a multiple instance learning approach based on a logical modeling. arXiv preprint arXiv:1805.01278 (2018)
  3. 3.
    Chen, J., Zaïane, O.R., Goebel, R.: Local community identification in social networks. In: ASONAM, pp. 237–242 (2009)Google Scholar
  4. 4.
    Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026132 (2005)CrossRefGoogle Scholar
  5. 5.
    Cleuziou, G., Dias, G.: Learning pretopological spaces for lexical taxonomy acquisition. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9285, pp. 493–508. Springer, Cham (2015). Scholar
  6. 6.
    Dalud-Vincent, M.: Une autre manière de modéliser les réseaux sociaux. Applications à l’étude de co-publications. Nouvelles perspectives en sciences sociales 12(2), 41–68 (2017)CrossRefGoogle Scholar
  7. 7.
    Danisch, M., Guillaume, J., Grand, B.L.: Towards multi-ego-centred communities: a node similarity approach. IJWBC 9(3), 299–322 (2013)CrossRefGoogle Scholar
  8. 8.
    Figueiredo, D.R., Ribeiro, L.F.R., Saverese, P.H.P.: struc2vec: learning node representations from structural identity. In: KDD (2017)Google Scholar
  9. 9.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, pp. 855–864. ACM (2016)Google Scholar
  10. 10.
    Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80(1), 016118 (2009)CrossRefGoogle Scholar
  11. 11.
    Lu, X., Kuzmin, K., Chen, M., Szymanski, B.K.: Adaptive modularity maximization via edge weighting scheme. Inf. Sci. 424(C), 55–68 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Luo, F., Wang, J.Z., Promislow, E.: Exploring local community structures in large networks. Web Intell. Agent Syst. 6(4), 387–400 (2008)CrossRefGoogle Scholar
  13. 13.
    Newman, M.E.: Modularity and community structure in networks. Proc. Nat. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  14. 14.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814 (2005)CrossRefGoogle Scholar
  15. 15.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gaëtan Caillaut
    • 1
    Email author
  • Guillaume Cleuziou
    • 1
  • Nicolas Dugué
    • 2
  1. 1.Université d’Orléans, INSA Centre Val de Loire, LIFO EA 4022OrléansFrance
  2. 2.Le Mans Université, LIUM, EA 4023Le MansFrance

Personalised recommendations