Wavelet-Based Clustering of Social-Network Users Using Temporal and Activity Profiles

  • Lipika Dey
  • Bhakti Gaonkar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


Encouraged by the success of social networking platforms, more and more enterprises are exploring the use of crowd-sourcing as a method for intra-organization knowledge management. There is not much information about their effectiveness though. While there has been some emphasis on studying friend networks, not much emphasis has been given towards understanding other kinds of user behavior like regularity of access or activity. In this paper we present a wavelet-based clustering method to cluster social-network users into different groups based on their temporal behavior and activity profiles. Cluster characterization reveals the underlying user-group characteristics. User data from web and enterprise social-network platforms have been analyzed.


Social Network Analysis Wavelet transformation Hierarchical K-means clustering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lipika Dey
    • 1
  • Bhakti Gaonkar
    • 1
  1. 1.Innovation LabsTata Consultancy ServicesDelhiIndia

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