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GroupMe: Supporting Group Formation with Mobile Sensing and Social Graph Mining

  • Bin Guo
  • Huilei He
  • Zhiwen Yu
  • Daqing Zhang
  • Xingshe Zhou
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 120)

Abstract

Nowadays, social activities in the real world (e.g., meetings, discussions, parties) are more and more popular and important to human life. As the number of contacts increases, the implicit social graph becomes increasingly complex, leading to a high cost on social activity organization and activity group formation. In order to promote the interaction among people and improve the efficiency of social activity organization, we propose a mobile social activity support system called GroupMe, which facilitates the activity group initiation based on mobile sensing and social graph mining. In GroupMe, user activities are automatically sensed and logged in the social activity logging (ACL) repository. By analyzing the historical ACL data through a series of group mining (group extraction, group abstraction) algorithms, we obtain implicit logical contact groups. We then use the sensed contexts and the computed user affinity to her logical groups to suggest highly relevant groups in social activity initiation. The experimental results verify the effectiveness of the proposed approach.

Keywords

Social graph mining context-awareness group formation and recommendation mobile sensing social activity organization 

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References

  1. 1.
    Kuhn, M., Wirz, M.: Cluestr: Mobile social networking for enhanced group communication. In: Proc. of the International Conference on Supporting Group Work, GROUP (2009)Google Scholar
  2. 2.
    MacLean, D., Hangal, S., Teh, S.K., Lam, M.S., Heer, J.: Groups without tears: mining social topologies from email. In: Proc. of IUI 2011, pp. 83–92 (2011)Google Scholar
  3. 3.
    Roth, M., et al.: Suggesting friends using the implicit social graph. In: Proc. of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010)Google Scholar
  4. 4.
    Whittaker, S., et al.: ContactMap: Organizing communication in a social desktop. ACM Transactions on Computer-Human Interaction (TOCHI) 11(4), 445–471 (2004)CrossRefGoogle Scholar
  5. 5.
    Guo, B., Zhang, D., Yang, D.: “Read” More from Business Cards: Toward a Smart Social Contact Management System. In: Proc. of WI 2011, pp. 384–387 (2011)Google Scholar
  6. 6.
    Eagle, N., Pentland, A.: Social Serendipity: Mobilizing Social Software. IEEE Pervasive Computing 4(2), 28–34 (2005)CrossRefGoogle Scholar
  7. 7.
    Zhang, D., Wang, Z., Guo, B., Raychoudhury, V., Zhou, X.: A Dynamic Community Creation Mechanism in Opportunistic Mobile Social Networks. In: Proc. of the Third IEEE International Conference on Social Computing (SocialCom 2011), pp. 509–514 (2011)Google Scholar
  8. 8.
    Boix, E.G., et al.: Flocks: enabling dynamic group interactions in mobile social networking applications. In: Proc. of SAC 2011, pp. 425–432 (2011)Google Scholar
  9. 9.
    Lubke, R., Schuster, D., Schill, A.: Mobilisgroups: Location-based group formation in mobile social networks. In: Proc. of PerCom Workshops, pp. 502–507 (2011)Google Scholar
  10. 10.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison Wesley (2005)Google Scholar
  11. 11.
    Liben, N.D., Kleinberg, J.: The link prediction problem for social networks. In: Proc. of CIKM 2003, pp. 556–559 (2003)Google Scholar
  12. 12.
    Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proc. of CHI 2009, pp. 211–220 (2009)Google Scholar
  13. 13.
    Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proc. of WWW 2010, pp. 981–990 (2010)Google Scholar
  14. 14.
    Guo, B., et al.: Enhancing Spontaneous Interaction in Opportunistic Mobile Social Networks. Communications in Mobile Computing (ComC) 1(6) (2012)Google Scholar
  15. 15.
    Kleinberg, J., Easley, D.: Networks, Crowds, and Markets. Cambridge University Press (2010)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Bin Guo
    • 1
  • Huilei He
    • 1
  • Zhiwen Yu
    • 1
  • Daqing Zhang
    • 1
  • Xingshe Zhou
    • 1
  1. 1.Northwestern Polytechnical UniversityXi’anChina

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