Wavelet-based clustering of social-network users using temporal and activity profiles

Authors:
Lipika Dey;Bhakti Gaonkar
Affiliations:
Innovation Labs, Tata Consultancy Services, Delhi, India;Innovation Labs, Tata Consultancy Services, Delhi, India
Venue:
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Year:
2011

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Abstract

Encouraged by the success of social networking platforms, more and more enterprises are exploring the use of crowd-sourcing as a method for intraorganization 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.