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A group evolving-based framework with perturbations for link prediction

Author

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  • Si, Cuiqi
  • Jiao, Licheng
  • Wu, Jianshe
  • Zhao, Jin

Abstract

Link prediction is a ubiquitous application in many fields which uses partially observed information to predict absence or presence of links between node pairs. The group evolving study provides reasonable explanations on the behaviors of nodes, relations between nodes and community formation in a network. Possible events in group evolution include continuing, growing, splitting, forming and so on. The changes discovered in networks are to some extent the result of these events. In this work, we present a group evolving-based characterization of node’s behavioral patterns, and via which we can estimate the probability they tend to interact. In general, the primary aim of this paper is to offer a minimal toy model to detect missing links based on evolution of groups and give a simpler explanation on the rationality of the model. We first introduce perturbations into networks to obtain stable cluster structures, and the stable clusters determine the stability of each node. Then fluctuations, another node behavior, are assumed by the participation of each node to its own belonging group. Finally, we demonstrate that such characteristics allow us to predict link existence and propose a model for link prediction which outperforms many classical methods with a decreasing computational time in large scales. Encouraging experimental results obtained on real networks show that our approach can effectively predict missing links in network, and even when nearly 40% of the edges are missing, it also retains stationary performance.

Suggested Citation

  • Si, Cuiqi & Jiao, Licheng & Wu, Jianshe & Zhao, Jin, 2017. "A group evolving-based framework with perturbations for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 117-128.
  • Handle: RePEc:eee:phsmap:v:475:y:2017:i:c:p:117-128
    DOI: 10.1016/j.physa.2017.01.087
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    References listed on IDEAS

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    1. L. Šubelj & M. Bajec, 2011. "Robust network community detection using balanced propagation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 81(3), pages 353-362, June.
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    3. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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