Link prediction based on heterogeneous degree penalization with extending neighbors and clustering coefficient

Rongrong Song, Guang Ling, Qingju Fan, Ming-Feng Ge () and Fang Wang ()
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Rongrong Song: School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China
Guang Ling: School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China
Qingju Fan: School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China
Ming-Feng Ge: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, P. R. China
Fang Wang: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 03, 1-21

Abstract: Link prediction, aiming to find missing links in a current network or to predict some possible new links in a future network, is a challenging problem in complex networks. Many existing link prediction algorithms perform the task by optimizing the node similarity measures, and then determining the possibility of the link between any pair of similar nodes. In this paper, we propose a novel node similarity index named heterogeneous degree penalization (HDP), which incorporates the quasi-local structure information of extending neighborhood of each pair of nodes to be predicted and the clustering coefficient of their common neighbors. For specific networks with different statistical properties, we can achieve a good performance of link prediction through adjusting the penalty weights. The experiment results show that, comparing with the other existing approaches, the proposed method can remarkably improve the accuracy of link prediction.

Keywords: Link prediction; heterogeneous degree penalization; extending neighborhood; clustering coefficient (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122500334

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