Community Analysis and Link Prediction in Dynamic Social Networks

  • Blaise Ngonmang
  • Emmanuel Viennet
  • Maurice Tchuente
  • Vanessa Kamga
Chapter

Abstract

Community detection and link prediction are two well-studied problems in social network analysis. They are interesting because they can be used as building blocks for other more complex problems like network visualisation or social recommendation. Because real networks are subject to constant evolution, these problems have also been extended to dynamic networks. This chapter presents an overview on these two problems.

Keywords

Community Structure Dynamic Network Quality Function Community Detection Link Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    L.A. Adamic, E. Adar, Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2003)CrossRefGoogle Scholar
  2. 2.
    O. Allali, C. Magnien, M. Latapy, Link prediction in bipartite graphs using internal links and weighted projection, in Proceedings of the Third International Workshop on Network Science for Communication Networks (NetSci- Com) (2011)Google Scholar
  3. 3.
    P. Auger, E. Kouokam, G. Sallet, M. Tchuente, B. Tsanou, The Ross–Macdonald model in a patchy environment. Math. Biosci. 216(2), 123–131 (2008)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    T. Aynaud, J.L. Guillaume, Static community detection algorithms for evolving networks, in WiOpt’10: Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, Avignon (2010), pp. 508–514. http://hal.inria.fr/inria-00492058
  5. 5.
    A.L. Barabasi, Linked: How Everything is Connected to Everything Else and What It Means, reissue edn. Plume, (2003)Google Scholar
  6. 6.
    A.L. Barabási, R. Albert, Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    V.D. Blondel, J. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10 10008 (2008)CrossRefGoogle Scholar
  8. 8.
    R. Campigotto, J.L. Guillaume, M. Seifi, The power of consensus: random graphs have no communities, in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ACM, New York, 2013), pp. 272–276Google Scholar
  9. 9.
    R. Cazabet, F. Amblard, Simulate to detect: a multi-agent system for community detection. IAT, 402–408. IEEE Computer Society (2011)Google Scholar
  10. 10.
    J. Chen, O.R. Zaiane, R. Goebel, Local communities identification in social networks, in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’09) (2009), pp. 237–242Google Scholar
  11. 11.
    A. Clauset, Finding local community structure in networks. Phys. Rev. 72, 026132 (2005)Google Scholar
  12. 12.
    M. Danisch, J.L. Guillaume, B.L. Grand, Towards multi-ego-centred communities: a node similarity approach. J. Web Based Communities 9(3), 299–322 (2013)CrossRefGoogle Scholar
  13. 13.
    K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea, A. Nanavati, A. Joshi, Social ties and their relevance to churn in mobile telecom networks, in EDBT ’08: Proceedings of the 11th International Conference on Extending Database Technology (2008), pp. 668–677Google Scholar
  14. 14.
    V. De Leo, G. Santoboni, F. Cerina, M. Mureddu, L. Secchi, A. Chessa, Community core detection in transportation networks. Phys. Rev. E 88(4), 042810 (2013)Google Scholar
  15. 15.
    S. Fortunato, Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    S. Fortunato, M. Barthélemy, Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(1), 36–41 (2007)CrossRefGoogle Scholar
  17. 17.
    A. Freno, C. Garriga Gemma, M. Keller, Learning to recommend links using graph structure and node content, in Neural Information Processing Systems Workshop on Choice Models and Preference Learning (2011)Google Scholar
  18. 18.
    E. Frías-Martínez, G. Williamson, V. Frías-Martínez, An agent-based model of epidemic spread using human mobility and social network information, in SocialCom/PASSAT (IEEE), pp. 57–64Google Scholar
  19. 19.
    A. Friggeri, G. Chelius, E. Fleury, Egomunities, exploring socially cohesive person-based communities. CoRR. abs/1102.2623 (2011)Google Scholar
  20. 20.
    D. Greene, D. Doyle, P. Cunningham, 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 ’10 (IEEE Computer Society, Washington, 2010), pp. 176–183Google Scholar
  21. 21.
    S. Gregory, Finding overlapping communities using disjoint community detection algorithms, in Complex Networks (2009), pp. 47–61Google Scholar
  22. 22.
    R. Guigourès, Utilisation des modèles de co-clustering pour l’analyse exploratoire des données. Ph.D. thesis in Applied mathematics, University of Paris 1 Panthéon SorbonneGoogle Scholar
  23. 23.
    M.A. Hasan, V. Chaoji, S. Salem, M. Zaki, Link prediction using supervised learning, in Proceedings of SDM 06 Workshop on Link Analysis, Counterterrorism and Security (2006)Google Scholar
  24. 24.
    H. Hwang, T. Jung, E. Suh, An ltv model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Syst. Appl. 26(2), 181–188 (2004)CrossRefGoogle Scholar
  25. 25.
    V. Kamga, M. Tchuente, E. Viennet, Prévision de liens dans les graphes bipartites avec attributs. Revue des Nouvelles Technologies de l’Information (RNTI-A6) (2013)Google Scholar
  26. 26.
    I. Keller, E. Viennet, A characterization of the modular structure of complex networks based on consensual communities, in 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS) (IEEE, Naples, Italy 2012), pp. 717–724Google Scholar
  27. 27.
    I. Keller, E. Viennet, A characterization of the modular structure of complex networks based on consensual communities, in 2013 International Conference on Signal-Image Technology Internet-Based Systems (SITIS) (2012), pp. 717–724Google Scholar
  28. 28.
    D. Liben-Nowell, J. Kleinberg, The link prediction problem for social networks, in Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM ’03 (ACM, New York, 2003), pp. 556–559Google Scholar
  29. 29.
    L. Lovász, Random walks on graphs: a survey. In Combinatorics, Paul Erdös is Eighty, eds. by D. Miklás, V.T. Sás, T. Szönyi (János Bolyai Mathematical Society, Budapest, 1996), pp. 353–398Google Scholar
  30. 30.
    L. Lu, T. Zhou, Link prediction in complex networks: a survey. Phys. A Stat. Mech. Appl. 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  31. 31.
    F. Luo, J.Z. Wang, E. Promislow, Exploring local community structure in large networks, in WI’06 (2006), pp. 233–239Google Scholar
  32. 32.
    G.R. Meleu, P. Melatagia, Analyse et modélisation du cari: croissance de la communauté de chercheurs du cari, in Conférence de Recheche en Informatique(CRI’2013), Yaoundé (2013), pp. 83–87Google Scholar
  33. 33.
    B. Mitra, L. Tabourier, C. Roth, Intrinsically dynamic network communities. CoRR. abs/1111.2018 (2011)Google Scholar
  34. 34.
    M.E.J. Newman, Clustering and preferential attachment in growing networks. Phys. Rev. E 64, 025102 (2001)CrossRefGoogle Scholar
  35. 35.
    M.E.J. Newman, The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)MathSciNetCrossRefMATHGoogle Scholar
  36. 36.
    M.E.J. Newman, Spectral methods for network community detection and graph partitioning. Phys. Rev. 884, 042822 (2013)Google Scholar
  37. 37.
    M. Newman, M. Girvan, Community structure in social and biological networks. Proc. Natl. Acad. Sci 99, 7821–7826 (2002)MathSciNetCrossRefMATHGoogle Scholar
  38. 38.
    B. Ngonmang, E. Viennet, Toward community dynamic through interactions prediction in complex networks, in 2013 International Conference on Signal-Image Technology Internet-Based Systems (SITIS) (2013), pp. 462–469Google Scholar
  39. 39.
    B. Ngonmang, M. Tchuente, E. Viennet, Local communities identification in social networks. Parallel Process. Lett. 22(1) (2012)Google Scholar
  40. 40.
    B. Ngonmang, E. Viennet, M. Tchuente, Churn prediction in a real online social network using local community analysis, in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’12) (2012), pp. 282–290Google Scholar
  41. 41.
    B. Ngonmang, S. Sean, R. Kirche, Monetization and services on a real online social network using social network analysis, in 2013 IEEE 13th International Conference on Data Mining Workshops (2013), pp. 185–193Google Scholar
  42. 42.
    N. Nguyen, T. Dinh, Y. Xuan, M. Thai, Adaptive algorithms for detecting community structure in dynamic social networks, in 2011 Proceedings IEEE INFOCOM (2011), pp. 2282–2290Google Scholar
  43. 43.
    G. Palla, I. Derényi, T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  44. 44.
    G. Palla, A.-L. Barabási, T. Vicsek, Quantifying social group evolution. Nature 446(7136), 664–667 (2007)CrossRefGoogle Scholar
  45. 45.
    P. Pons, M. Latapy, Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10(2), 191–218 (2006)MathSciNetCrossRefMATHGoogle Scholar
  46. 46.
    M. Seifi, I. Junier, J.B. Rouquier, S. Iskrov, J.L. Guillaume, Stable community cores in complex networks, in Complex Networks. Studies in Computational Intelligence, vol. 424 (Springer, Berlin/Heidelberg, 2013), pp. 87–98Google Scholar
  47. 47.
    A. Sidiki, M. Tchuente, An analytical formula for the basic reproduction number on cellular sir networks, in Actes du Colloque Africain de Recherche en Informatique, (2012)Google Scholar
  48. 48.
    H.A. Simon, On a class of skew distribution functions. Biometrika 42, 198–216 (1955)CrossRefGoogle Scholar
  49. 49.
    I. Simonsen, K.A. Eriksen, S. Maslov, K. Sneppen, Diffusion on complex networks: a way to probe their large-scale topological structures. Phys. A Stat. Theor. Phys. 336(1–2), 163–173 (2004)CrossRefGoogle Scholar
  50. 50.
    C. Tantipathananandh, T. Berger-Wolf, D. Kempe, A framework for community identification in dynamic social networks, in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’07 (ACM, New York, 2007), pp. 717–726Google Scholar
  51. 51.
    D. Watts, S. Strogatz, Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  52. 52.
    T. Zhou, L. Lü, Y.C. Zhang, Predicting missing links via local information. Eur. Phys. J. B 71, 623 (2009)CrossRefMATHGoogle Scholar
  53. 53.
    H. Zhu, W. Kinzel, Antipredictable sequences: harder to predict than random sequences. Neural Comput. 10, 2219–2230 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Blaise Ngonmang
    • 1
    • 2
  • Emmanuel Viennet
    • 1
  • Maurice Tchuente
    • 2
  • Vanessa Kamga
    • 1
    • 2
  1. 1.Université de Paris 13VilletaneuseFrance
  2. 2.Université de Yaoundé 1YaoundéCameroon

Personalised recommendations