A new method for predicting future links in temporal networks based on node influence

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

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 12, 1-21

Abstract: Link prediction in temporal networks has always been a hot topic in both statistical physics and network science. Most existing works fail to consider the inner relationship between nodes, leading to poor prediction accuracy. Even though a wide range of realistic networks are temporal ones, few existing works investigated the properties of realistic and temporal networks. In this paper, we address the problem of abstracting individual attributes and propose a adaptive link prediction method for temporal networks based on H-index to predict future links. The matching degree of nodes is first defined considering both the native influence and the secondary influence of local structure. Then a similarity index is designed using a decaying parameter to punish the snapshots with their occurring time. Experimental results on five realistic temporal networks observing consistent gains of 2–9% AUC in comparison to the best baseline in four networks show that our proposed method outperforms several benchmarks under two standard evaluation metrics: AUC and Ranking score. We also investigate the influence of the free parameter and the definition of matching degree on the prediction performance.

Keywords: Temporal network; link prediction; matching degree; node influence (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0129183121501606

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