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A class-based link prediction using Distance Dependent Chinese Restaurant Process

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  • Andalib, Azam
  • Babamir, Seyed Morteza

Abstract

One of the important tasks in relational data analysis is link prediction which has been successfully applied on many applications such as bioinformatics, information retrieval, etc. The link prediction is defined as predicting the existence or absence of edges between nodes of a network. In this paper, we propose a novel method for link prediction based on Distance Dependent Chinese Restaurant Process (DDCRP) model which enables us to utilize the information of the topological structure of the network such as shortest path and connectivity of the nodes. We also propose a new Gibbs sampling algorithm for computing the posterior distribution of the hidden variables based on the training data. Experimental results on three real-world datasets show the superiority of the proposed method over other probabilistic models for link prediction problem.

Suggested Citation

  • Andalib, Azam & Babamir, Seyed Morteza, 2016. "A class-based link prediction using Distance Dependent Chinese Restaurant Process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 204-214.
    • Handle: RePEc:eee:phsmap:v:456:y:2016:i:c:p:204-214
    DOI: 10.1016/j.physa.2016.03.067
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    References listed on IDEAS

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    1. P. Damlen & J. Wakefield & S. Walker, 1999. "Gibbs sampling for Bayesian non‐conjugate and hierarchical models by using auxiliary variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 331-344, April.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
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    2. Assouli, Nora & Benahmed, Khelifa & Gasbaoui, Brahim, 2021. "How to predict crime — informatics-inspired approach from link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).

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