Users in Volatile Communities: Studying Active Participation and Community Evolution

  • Tanja Falkowski
  • Myra Spiliopoulou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


Active participation of a person in a community is a powerful indicator of the person’s interests, preferences, beliefs and (often) social and demographic context. Community membership is part of a user’s model and can contribute to tasks like personalized services, assistance and recommendations. However, a community member can be active or inactive. To what extend is a community still representative of the interests of an inactive participant? To gain insights to this question, we observe a community as an evolving social structure and study the effects of member fluctuation. We define a community as a high-level temporal structure composed of “community instances” that are defined conventionally through observable active participation and are captured at distinct timepoints. Thus, we capture community volatility, as evolution and discontinuation. This delivers us clues about the role of the community for its members, both for active and inactive ones. We have applied our model on a community exhibiting large fluctuation of members and acquired insights on the community-member interplay.


user communities community evolution community participation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aggarwal, C.C., Yu, P.S.: Online Analysis of Community Evolution in Data Streams. In: Proceedings of SIAM International Data Mining Conference (2005)Google Scholar
  2. 2.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group Formation in Large Social Networks: Membership, Growth, and Evolution. In: Proc. of KDD 2006 (2006)Google Scholar
  3. 3.
    Cortes, C., Pregibon, D., Volinsky, C.: Communities of Interest. In: Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis, pp. 105–114 (2001)Google Scholar
  4. 4.
    Dunne, J.A., Williams, R.J., Martinez, N.D.: Food-web structure and network theory: The role of connectance and size. PNAS 99(20), 12917–12922 (2002)CrossRefGoogle Scholar
  5. 5.
    Falkowski, T., Bartelheimer, J., Spiliopoulou, M.: Mining and Visualizing the Evolution of Subgroups in Social Networks. In: Proc. of IEEE/WIC/ACM International Conference on Web Intelligence (WI-06) (2006)Google Scholar
  6. 6.
    Falkowski, T., Spiliopoulou, M.: Observing Dynamics in Community Structures. In: Proc. of Adaptation in Artificial and Biological Systems (AISB 2006), pp. 102–105 (2006)Google Scholar
  7. 7.
    Gibson, D., Kleinberg, J., Raghavan, P.: Inferring Web Communities from Link Topology. In: Proceedings of the 9th ACM Conference on Hypertext and Hypermedia (1998)Google Scholar
  8. 8.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Gloor, P.A., Zhao, Y.: TeCFlow- A Temporal Communication Flow Visualizer for Social Networks Analysis. In: CSCW 2004 Workshop on Social Networks, ACM (2004)Google Scholar
  10. 10.
    Jeong, H., Néda, Z., Barabási, A.-L.: Measuring preferential attachment in evolving networks. Europhysics Letters 61(4), 567–572 (2003)CrossRefGoogle Scholar
  11. 11.
    Kleinberg, J., Lawrence, S.: The Structure of the Web. Science 294, 1849–1850 (2001)CrossRefGoogle Scholar
  12. 12.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. In: Proc. of KDD 2005 (2005)Google Scholar
  13. 13.
    Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In: Proc. of KDD 2005, pp. 198–207 (2005)Google Scholar
  14. 14.
    Moody, J., Mc Farland, D., Bender-deMoll, S.: Dynamic Network Visualization. American Journal of Sociology 110(4), 1206–1241 (2005)CrossRefGoogle Scholar
  15. 15.
    Newman, M.: The structure of scientific collaboration networks, PNAS, 98(2) (2001)Google Scholar
  16. 16.
    Newman, M., Girvan, M.: Finding and evaluating community structure in networks, Physical Review, E 69(026113) (2004)Google Scholar
  17. 17.
    Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., Schult, R.: MONIC - Modeling and Monitoring Cluster Transitions. In: Proc. of KDD 2006, pp. 706–711 (2006)Google Scholar
  18. 18.
    Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as Spectroscopy: Automated Discovery of Community Structure within Organizations. In: Huysman, M., Wenger, E., Wulf, V. (eds.) Communities and Technologies, Kluwer, Dordrecht (2003)Google Scholar
  19. 19.
    Wilkinson, D.M., Huberman, B.A.: A method for finding communities of related genes. In: Proc. National Academy of Sciences U.S.A., vol. 10(1073) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tanja Falkowski
    • 1
  • Myra Spiliopoulou
    • 1
  1. 1.Otto-von-Guericke-Universität Magdeburg, Faculty of Computer Science, Universitätsplatz 2, 39106 MagdeburgGermany

Personalised recommendations