Survey on Social Ego-Community Detection

  • Ahmed Ould Mohamed MoctarEmail author
  • Idrissa Sarr
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)


Community detection is one of the main topics of social network analysis, which is attracting increasing attention from many researchers. In fact, the community detection can be done either from the global network (such is the case of global communities), or from some specific nodes: case of ego-communities. The early community detection works focused on network partitioning into several global communities. Over time, researchers have been interested in studying ego-communities to analyze the impact of interest nodes within network. Even if the global community survey is very well covered through works like that of Fortunato; that relating to ego-communities is not yet. The purpose of this paper is to propose a survey on ego-community detection approaches in order to reduce the survey lack while focusing on the strengths and weaknesses of existing solutions.


Community Detection Ego-community Step neighborhood Survey 


  1. 1.
    Bron, C., Kerbosch, J.: Algorithm 457: finding all cliques of an undirected graph. Commun. ACM 16(9), 575–577 (1973)CrossRefGoogle Scholar
  2. 2.
    Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026,132 (2005)Google Scholar
  3. 3.
    Danisch, M.: Mesures de proximité appliquées à la détection de communautés dans les grands graphes de terrain. Ph.D. thesis, Paris 6 (2015)Google Scholar
  4. 4.
    Danisch, M., Guillaume, J.L., Le Grand, B.: Multi-ego-centered communities in practice. Soc. Netw. Anal. Min. 4(1), 1–10 (2014)CrossRefGoogle Scholar
  5. 5.
    Ding, X., Zhang, J., Yang, J.: A robust two-stage algorithm for local community detection. Knowl.-Based Syst. 152 (2018)Google Scholar
  6. 6.
    Fagnan, J., Zaiane, O., Barbosa, D.: Using triads to identify local community structure in social networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 108–112. IEEE (2014)Google Scholar
  7. 7.
    Fanrong, M., Mu, Z., Yong, Z., Ranran, Z.: Local community detection in complex networks based on maximum cliques extension. Math. Probl. Eng. 2014 (2014)Google Scholar
  8. 8.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hamann, M., Röhrs, E., Wagner, D.: Local community detection based on small cliques. Algorithms 10(3), 90 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Huang, J., Sun, H., Liu, Y., Song, Q., Weninger, T.: Towards online multiresolution community detection in large-scale networks. PloS One 6(8), e23,829 (2011)CrossRefGoogle Scholar
  11. 11.
    Kaple, M., Kulkarni, K., Potika, K.: Viral marketing for smart cities: influencers in social network communities. In: 9th IEEE International Workshop on Big Data Appications in Smart City Development (2017)Google Scholar
  12. 12.
    Kloster, K., Gleich, D.F.: Heat kernel based community detection. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1386–1395. ACM (2014)Google Scholar
  13. 13.
    Liu, J., Wang, D., Zhao, W., Feng, S., Yifei, S.: A unified framework of lightweight local community detection for different node similarity measurement. In: Chinese National Conference on Social Media Processing, pp. 283–295. Springer (2017)Google Scholar
  14. 14.
    Lu, Z., Wen, Y., Cao, G.: Community detection in weighted networks: Algorithms and applications. In: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 179–184. IEEE (2013)Google Scholar
  15. 15.
    Luo, F., Wang, J.Z., Promislow, E.: Exploring local community structures in large networks. Web Intell. Agent Syst. Int. J. 6(4), 387–400 (2008)Google Scholar
  16. 16.
    Moctar, A.O.M., Sarr, I.: Ego-centered community detection in directed and weighted networks. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, pp. 1201–1208. ACM, New York, NY, USA (2017).
  17. 17.
    Moctar, A.O.M., Sarr, I.: Building ego-community based on a non-closed neighborhood. In: 14th African Conference on Research in Computer Science and Applied Mathematics. Stellenbosch, South Africa (2018). To appearGoogle Scholar
  18. 18.
    Ngonmang, B., Tchuente, M., Viennet, E.: Local community identification in social networks. Parallel Process. Lett. 22(01), 1240,004 (2012)Google Scholar
  19. 19.
    Ratnayake, R., Crowe, S.J., Jasperse, J., Privette, G., Stone, E., Miller, L., Hertz, D., Fu, C., Maenner, M.J., Jambai, A., et al.: Assessment of community event-based surveillance for ebola virus disease, sierra leone, 2015. Emerg. Infect. Dis. 22(8), 1431 (2016)CrossRefGoogle Scholar
  20. 20.
    Tsourakakis, C., Bonchi, F., Gionis, A., Gullo, F., Tsiarli, M.: Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 104–112. ACM (2013)Google Scholar
  21. 21.
    Wu, Y., Jin, R., Li, J., Zhang, X.: Robust local community detection: on free rider effect and its elimination. Proc. VLDB Endow. 8(7), 798–809 (2015)CrossRefGoogle Scholar
  22. 22.
    Xiang, J., Hu, T., Zhang, Y., Hu, K., Li, J.M., Xu, X.K., Liu, C.C., Chen, S.: Local modularity for community detection in complex networks. Phys. A: Stat. Mech. Appl. 443, 451–459 (2016)CrossRefGoogle Scholar
  23. 23.
    Zheng, W., Zhao, X., Kang, Z.: Analysis of associtivity and community structure in mobile social networks. Procedia Comput. Sci. 107, 630–635 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Cheikh Anta Diop UniversityDakar - Fann BP, 5005Senegal

Personalised recommendations