Link prediction based on local weighted paths for complex networks

Yabing Yao (), Ruisheng Zhang, Fan Yang (), Yongna Yuan (), Rongjing Hu () and Zhili Zhao ()
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Yabing Yao: School of Information Science and Engineering, Lanzhou University, Lan zhou, Gansu 730000, P. R. China
Ruisheng Zhang: School of Information Science and Engineering, Lanzhou University, Lan zhou, Gansu 730000, P. R. China
Fan Yang: School of Information Science and Engineering, Lanzhou University, Lan zhou, Gansu 730000, P. R. China
Yongna Yuan: School of Information Science and Engineering, Lanzhou University, Lan zhou, Gansu 730000, P. R. China
Rongjing Hu: School of Information Science and Engineering, Lanzhou University, Lan zhou, Gansu 730000, P. R. China
Zhili Zhao: School of Information Science and Engineering, Lanzhou University, Lan zhou, Gansu 730000, P. R. China

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 04, 1-23

Abstract: As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

Keywords: Complex networks; link degree; link prediction; node similarity; path weight (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S012918311750053X

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