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Link Prediction in Temporal Heterogeneous Networks

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Intelligence and Security Informatics (PAISI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10241))

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Abstract

Link prediction in temporal social networks addresses the problem of predicting future links. The problem of link prediction in heterogeneous networks is challenging due to the existence of multiple types of nodes and edges. There are many methods available in the literature for homogeneous networks, which rely on the network topology. In this work, we extend some of the standard measures viz Common Neighbors, Jaccard Coefficient, AdamicAdar, Time-score, Co-occurrence probabilistic measure and Temporal Co-occurrence probabilistic measure to heterogeneous networks. Probabilistic graphical models prove to be efficient for link prediction compared to topological methods. We incorporate the information related to time of link formation into probabilistic graphical models and generate a new measure called Heterogeneous Temporal Co-occurrence probability (Hetero-TCOP) measure for heterogeneous networks. We evaluate all the extended heterogeneous measures along with Hetero-TCOP on DBLP and HiePh bibliographic networks for predicting two types of links: author-conference/journal links and co-author links in the heterogeneous environment. In both cases, Hetero-TCOP achieves superior performance over the standard topological measures. In the case of DBLP dataset, Hetero-TCOP shows an improvement of 15% accuracy over neighborhood-based measures, 6% over temporal measures and 5% over Co-occurrence probability measure. Similar improvement in performance is observed for HeiPh dataset also.

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Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    T. Jaya Lakshmi & S. Durga Bhavani

  2. Vasireddy Venkatadri Institute of Technology, Nambur, Andhra Pradesh, India

    T. Jaya Lakshmi

Authors
  1. T. Jaya Lakshmi
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  2. S. Durga Bhavani
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Correspondence to T. Jaya Lakshmi .

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Editors and Affiliations

  1. Virginia Tech , Blacksburg, Virginia, USA

    G. Alan Wang

  2. The University of Hong Kong, Hong Kong, China

    Michael Chau

  3. The University of Arizona, Tucson, Arizona, USA

    Hsinchun Chen

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Jaya Lakshmi, T., Durga Bhavani, S. (2017). Link Prediction in Temporal Heterogeneous Networks. In: Wang, G., Chau, M., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2017. Lecture Notes in Computer Science(), vol 10241. Springer, Cham. https://doi.org/10.1007/978-3-319-57463-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-57463-9_6

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  • Online ISBN: 978-3-319-57463-9

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