Predicting missing links in directed networks based on local network structure and investment theory

Jinsong Li (), Jianhua Peng (), Shuxin Liu and Xinsheng Ji ()
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Jinsong Li: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China
Jianhua Peng: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China
Shuxin Liu: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China
Xinsheng Ji: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 07, 1-26

Abstract: As an elementary task in statistical physics and network science, link prediction has attracted great attention of researchers from many fields. While numerous similarity-based indices have been designed for undirected networks, link prediction in directed networks has not been thoroughly studied yet. Among several representative works, motif predictors such as “feed-forward-loop” and Bi-fan predictor perform well in both accuracy and efficiency. Nevertheless, they fail to explicitly explain the linkage motivation of nodes, nor do they consider the unequal contributions of different neighbors between node pairs. In this paper, motivated by the investment theory in economics, we propose a universal and explicable model to quantify the contributions of nodes on driving link formation. Based on the analysis on two typical investment relationships, namely “follow-up” and “co-follow”, an investment-profit index is designed for link prediction in directed networks. Empirical studies on 12 static networks and four temporal networks show that the proposed method outperforms eight mainstream baselines under three standard metrics. As a quasi-local index, it is also suitable for large-scale networks.

Keywords: Statistical physics; directed networks; link prediction; investment theory (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120500965

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