World Wide Web

, Volume 22, Issue 4, pp 1819–1854 | Cite as

Discovering and tracking query oriented active online social groups in dynamic information network

  • Md Musfique AnwarEmail author
  • Chengfei Liu
  • Jianxin Li
Part of the following topical collections:
  1. Special Issue on Social Computing and Big Data Applications


The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies.


Active social groups Dynamic social networks Fading time window Group evolution 



This work is supported by the ARC Discovery Projects DP170104747, DP160102412 and DP160102114.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Software EngineeringSwinburne University of TechnologyMelbourneAustralia
  2. 2.Department of Computer Science and Software EngineeringThe University of Western AustraliaPerthAustralia

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