SONIC-MAN: A Distributed Protocol for Dynamic Community Detection and Management

  • Barbara GuidiEmail author
  • Andrea Michienzi
  • Laura Ricci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10853)


The study of complex networks has acquired great importance during the last years because of the diffusion of several phenomena which can be described by these networks. Community detection is one of the most investigated problem in this area, however only a few solutions for detecting communities in a distributed and dynamic environment have been presented. In this paper we propose SONIC-MAN, a distributed protocol to detect and manage communities in a peer-to-peer dynamic environment. Our approach is particularly targeted to distributed online social networks and its main goal is to discover communities in the ego-network of the users. SONIC-MAN is based on a Temporal Trade-off approach and exploits a set of super-peers for the management of the communities. The paper presents a set of evaluations proving that SONIC-MAN is able to detect dynamic communities in a distributed setting and to return results close a centralized approach based on the same basic algorithm for community discovering.


Peer to Peer Community detection Complex networks Decentralized online social networks 


  1. 1.
    Aynaud, T., Fleury, E., Guillaume, J.L., Wang, Q.: Communities in evolving networks: definitions, detection, and analysis techniques. In: Mukherjee, A., Choudhury, M., Peruani, F., Ganguly, N., Mitra, B. (eds.) Dynamics On and Of Complex Networks, Volume 2. Modeling and Simulation in Science, Engineering and Technology, pp. 159–200. Springer, New York (2013).
  2. 2.
    Cazabet, R., Amblard, F.: Dynamic community detection. In: Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining, pp. 404–414. Springer, New York (2014). Scholar
  3. 3.
    Clementi, A.E.F., Ianni, M.D., Gambosi, G., Natale, E., Silvestri, R.: Distributed community detection in dynamic graphs. CoRR abs/1302.5607 (2013)Google Scholar
  4. 4.
    Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. Stat. Anal. Data Min. ASA Data Sci. J. 4(5), 512–546 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Datta, A., Buchegger, S., Vu, L.H., Strufe, T., Rzadca, K.: Decentralized online social networks. In: Furht, B. (ed.) Handbook of Social Network Technologies and Applications, pp. 349–378. Springer, Boston (2010). Scholar
  6. 6.
    De Salve, A., Dondio, M., Guidi, B., Ricci, L.: The impact of user’s availability on On-line Ego Networks. Comput. Commun. 73(PB), 211–218 (2016)CrossRefGoogle Scholar
  7. 7.
    De Salve, A., Guidi, B., Ricci, L.: Evaluation of structural and temporal properties of ego networks for data availability in DOSNS. Mobile Netw. Appl. 23(1), 155–166 (2018)CrossRefGoogle Scholar
  8. 8.
    Everett, M., Borgatti, S.: Ego network betweenness. Soc. Netw. 27, 31–38 (2005)CrossRefGoogle Scholar
  9. 9.
    Fischer, M.J., Lynch, N.A., Paterson, M.S.: Impossibility of distributed consensus with one faulty process. J. ACM 32(2), 374–382 (1985)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Fortunato, S.: Community detection in graphs. CoRR abs/0906.0612 (2009)Google Scholar
  11. 11.
    Guidi, B., Amft, T., Salve, A.D., Graffi, K., Ricci, L.: Didusonet: a P2P architecture for distributed dunbar-based social networks. Peer-to-Peer Netw. Appl. 9(6), 1177–1194 (2016)CrossRefGoogle Scholar
  12. 12.
    Guidi, B., Michienzi, A., Rossetti, G.: Dynamic community analysis in decentralized online social networks. In: Heras, D.B., Bougé, L. (eds.) Euro-Par 2017. LNCS, vol. 10659, pp. 517–528. Springer, Cham (2018). Scholar
  13. 13.
    Herbiet, G.J., Bouvry, P.: Sharc: Community-based partitioning for mobile ad hoc networks using neighborhood similarity. In: 2010 IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, pp. 1–9 (2010)Google Scholar
  14. 14.
    Hui, P., Yoneki, E., Chan, S.Y., Crowcroft, J.: Distributed community detection in delay tolerant networks. In: Proceedings of 2nd ACM/IEEE International Workshop on Mobility in the Evolving Internet Architecture, pp. 1–8 (2007)Google Scholar
  15. 15.
    Montresor, A., Jelasity, M.: PeerSim: a scalable P2P simulator. In: Proceedings of the 9th International Conference on Peer-to-Peer (P2P 2009), pp. 99–100, September 2009Google Scholar
  16. 16.
    Palla, G., Barabási, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)CrossRefGoogle Scholar
  17. 17.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRefGoogle Scholar
  18. 18.
    Ramaswamy, L., Gedik, B., Liu, L.: A distributed approach to node clustering in decentralized peer-to-peer networks. IEEE Trans. Parallel Distrib. Syst. 16(9), 814–829 (2005)CrossRefGoogle Scholar
  19. 19.
    Rossetti, G., Cazabet, R.: Community discovery in dynamic networks: a survey. Technical report (2017)Google Scholar
  20. 20.
    Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F.: Tiles: an online algorithm for community discovery in dynamic social networks. Mach. Learn. 106(8), 1213–1241 (2017)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Takaffoli, M., Sangi, F., Fagnan, J., Zaïane, O.R.: MODEC-modeling and detecting evolutions of communities. In: 5th International Conference on Weblogs and Social Media (ICWSM), pp. 30–41. AAAI (2011)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly

Personalised recommendations