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Revealing how network structure affects accuracy of link prediction

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  • Jin-Xuan Yang

    (School of Mathematical Sciences, MOE-LSC and SHL-MAC, Shanghai Jiao Tong University)

  • Xiao-Dong Zhang

    (School of Mathematical Sciences, MOE-LSC and SHL-MAC, Shanghai Jiao Tong University)

Abstract

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.

Suggested Citation

  • Jin-Xuan Yang & Xiao-Dong Zhang, 2017. "Revealing how network structure affects accuracy of link prediction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(8), pages 1-8, August.
    • Handle: RePEc:spr:eurphb:v:90:y:2017:i:8:d:10.1140_epjb_e2017-70599-4
    DOI: 10.1140/epjb/e2017-70599-4
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    Statistical and Nonlinear Physics;

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