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Discovering Communities in Heterogeneous Social Networks Based on Non-negative Tensor Factorization and Cluster Ensemble Approach

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Mining Intelligence and Knowledge Exploration (MIKE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9468))

Abstract

Identification of the appropriate community structure in social networks is an arduous task. The intricacy of the problem increases with the heterogeneity of multiple types of objects and relationships involved in the analysis of the network. Traditional approaches for community detection focus on the networks comprising of content features and linkage information of the set of single type of entities. However, rich social media networks are usually heterogeneous in nature with multiple types of relationships existing between different types of entities. Cognizant to these requirements, we develop a model for community detection in Heterogeneous Social Networks (HSNs) employing non-negative tensor factorization method and cluster ensemble approach. Extensive experiments are performed on 20Newsgroup dataset which establish the effectiveness and efficiency of our scheme.

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Ankita Verma & K. K. Bharadwaj

Authors
  1. Ankita Verma
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  2. K. K. Bharadwaj
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Correspondence to Ankita Verma .

Editor information

Editors and Affiliations

  1. Norwegian Univ. of Science & Technology, Trondheim, Norway

    Rajendra Prasath

  2. Intl Inst of Info Tech Hyderabad, Hyderabad, India

    Anil Kumar Vuppala

  3. V.H.N.S.N.College (Autonomous), Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Verma, A., Bharadwaj, K.K. (2015). Discovering Communities in Heterogeneous Social Networks Based on Non-negative Tensor Factorization and Cluster Ensemble Approach. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26831-6

  • Online ISBN: 978-3-319-26832-3

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