Dynamic Community Analysis in Decentralized Online Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10659)

Abstract

Community structure is one of the most studied features of Online Social Networks (OSNs). Community detection guarantees several advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. One of the main challenge in DOSNs concerns the availability of social data and communities can be exploited to guarantee a more efficient solution about the data availability problem. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic social networks, such as DOSNs, where the online/offline status of user changes very frequently. In this paper, we focus our attention on a preliminary analysis of dynamic community detection in DOSNs by studying a real Facebook dataset to evaluate how frequent the communities change over time and which events are more frequent. The results prove that the social graph has a high instability and distributed solutions to manage the dynamism are needed.

Keywords

Decentralized Online Social Networks P2P Dynamic community Data availability 

References

  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, vol. 2, pp. 159–200. Springer, New York (2013).  https://doi.org/10.1007/978-1-4614-6729-8_9 Google Scholar
  2. 2.
    Buchegger, S., Schioberg, D., Vu, L., Datta, A.: Implementing a P2P social network - early experiences and insights from PeerSoN. In: Second ACM Workshop on Social Network Systems (Co-located with EuroSys 2009) (2009)Google Scholar
  3. 3.
    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).  https://doi.org/10.1007/978-1-4614-6170-8 Google Scholar
  4. 4.
    Cazabet, R., Amblard, F., Hanachi, C.: Detection of overlapping communities in dynamical social networks. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 309–314. IEEE (2010)Google Scholar
  5. 5.
    Coscia, M., Rossetti, G., Giannotti, F., Pedreschi, D.: DEMON: a local-first discovery method for overlapping communities. In: Proceedings of the 18th ACM SIGKDD, KDD 2012 (2012)Google Scholar
  6. 6.
    Cutillo, L.A., Molva, R., Strufe, T.: Safebook: a privacy-preserving online social network leveraging on real-life trust. Commun. Mag. 47(12), 94–101 (2009)CrossRefGoogle Scholar
  7. 7.
    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).  https://doi.org/10.1007/978-1-4419-7142-5_17 CrossRefGoogle Scholar
  8. 8.
    De Salve, A., Dondio, M., Guidi, B., Ricci, L.: The impact of user’s availability on on-line ego networks: a Facebook analysis. Comput. Commun. 73, 211–218 (2016)CrossRefGoogle Scholar
  9. 9.
    De Salve, A., Guidi, B., Mori, P., Ricci, L.: Distributed coverage of ego networks in F2F online social networks. In: 2016 International IEEE Conferences on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 423–431 (2016)Google Scholar
  10. 10.
    Graffi, K., Gross, C., Mukherjee, P., Kovacevic, A., Steinmetz, R.: LifeSocial. KOM: a P2P-based platform for secure online social networks. In: Peer-to-Peer Computing, pp. 1–2. IEEE (2010)Google Scholar
  11. 11.
    Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities in dynamic social networks. In: Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2010, pp. 176–183 (2010)Google Scholar
  12. 12.
    Guidi, B., Amft, T., De Salve, A., Graffi, K., Ricci, L.: DiDuSoNet: a P2P architecture for distributed dunbar-based social networks. Peer-to-Peer Netw. Appl. 9(6), 1–18 (2015)Google Scholar
  13. 13.
    Guidi, B., Conti, M., Ricci, L.: P2P architectures for distributed online social networks. In: 2013 International Conference on High Performance Computing and Simulation (HPCS), pp. 678–681. IEEE (2013)Google Scholar
  14. 14.
    Hartmann, T., Kappes, A., Wagner, D.: Clustering evolving networks. In: Kliemann, L., Sanders, P. (eds.) Algorithm Engineering. LNCS, vol. 9220, pp. 280–329. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49487-6_9 CrossRefGoogle Scholar
  15. 15.
    Marsden, P.: Egocentric and sociocentric measures of network centrality. Soc. Netw. 24(4), 407–422 (2002)CrossRefGoogle Scholar
  16. 16.
    Narendula, R., Papaioannou, T.G., Aberer, K.: My3: a highly-available P2P-based online social network. In: 2011 IEEE International Conference on Peer-to-Peer Computing (P2P), pp. 166–167. IEEE (2011)Google Scholar
  17. 17.
    Nilizadeh, S., Jahid, S., Mittal, P., Borisov, N., Kapadia, A.: Cachet: a decentralized architecture for privacy preserving social networking with caching. In: Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2012, pp. 337–348. ACM (2012)Google Scholar
  18. 18.
    Palla, G., Barabási, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Salve, A.D., Guidi, B., Ricci, L.: Evaluation of structural and temporal properties of ego networks for data availability in DOSNs. Mob. Netw. Appl. 1–12 (2017)Google Scholar
  21. 21.
    Takaffoli, M., Rabbany, R., Zaïane, O.R.: Community evolution prediction in dynamic social networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 9–16 (2014)Google Scholar
  22. 22.
    Takaffoli, M., Sangi, F., Fagnan, J., Zäıane, O.R.: Community evolution mining in dynamic social networks. Procedia-Soc. Behav. Sci. 22, 49–58 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Barbara Guidi
    • 1
  • Andrea Michienzi
    • 1
  • Giulio Rossetti
    • 1
  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly

Personalised recommendations