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Community-Driven Social Influence Analysis and Applications

  • Yang ZhangEmail author
  • Jun Pang
Conference paper
  • 2.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9114)

Abstract

Nowadays, people conduct a lot of activities with their online social networks. With the large amount of social data available, quantitative analysis of social influence becomes feasible. In this PhD project, we aim to study users’ social influence at the community level, mainly because users in social networks are naturally organized in communities and communities play fundamental roles in understanding social behaviors and social phenomenons. Through experiments with a location-based social networks dataset, we start by demonstrating communities’ influence on users’ mobility, and then we focus on the influence of leaders in the communities. As a next step, we intend to detect users that act as structural hole spanners and analyze their social influence across different communities. Based on these studies, we plan to propose a unified approach to quantify users’ social influence and investigate its applications, for example, in social interaction and behavior analysis.

Keywords

Social Network Social Influence Community Detection Online Social Network Community Detection Algorithm 
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.
    Tang, J., Chang, Y., Liu, H.: Mining social media with social theories: a survey. SIGKDD Explorations Newsletter 15(2), 20–29 (2014)CrossRefGoogle Scholar
  2. 2.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proc. 4th ACM International Conference on Web Search and Data Mining (WSDM), pp. 65–74. ACM Press, New York (2011)Google Scholar
  3. 3.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proc. 9th ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 137–146. ACM Press, New York (2003)Google Scholar
  4. 4.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proc. 3rd ACM International Conference on Web Search and Data Mining (WSDM), pp. 241–250. ACM Press, New York (2010)Google Scholar
  5. 5.
    Pang, J., Zhang, Y.: Location prediction: communities speak louder than friends. CoRR abs/1408.1228 (2014)Google Scholar
  6. 6.
    Pang, J., Zhang, Y.: Exploring communities for effective location prediction (poster paper). In: Proc. 24th World Wide Web Conference (Companion Volume) (WWW). ACM Press, New York (2015) (accepted)Google Scholar
  7. 7.
    Lou, T., Tang, J.: Mining structural hole spanners through information diffusion in social networks. In: Proc. 22nd International Conference on World Wide Web (WWW), pp. 825–836. ACM Press, New York (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Science, Technology and CommunicationUniversity of LuxembourgLuxembourgLuxembourg

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