An effective similarity measure based on kernel spectral method for complex networks

Longjie Li, Lu Wang (), Shenshen Bai (), Shiyu Fang (), Jianjun Cheng () and Xiaoyun Chen ()
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Longjie Li: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China
Lu Wang: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China
Shenshen Bai: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China
Shiyu Fang: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China
Jianjun Cheng: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China
Xiaoyun Chen: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 07, 1-21

Abstract: Node similarity measure is a special important task in complex network analysis and plays a critical role in a multitude of applications, such as link prediction, community detection, and recommender systems. In this study, we are interested in link-based similarity measures, which only concern the structural information of networks when estimating node similarity. A new algorithm is proposed by adopting the idea of kernel spectral method to quantify the similarity of nodes. When computing the kernel matrix, the proposed algorithm makes use of local structural information, but it takes advantage of global information when constructing the feature matrix. Thence, the proposed algorithm could better capture potential relationships between nodes. To show the superiority of our algorithm over others, we conduct experiments on 10 real-world networks. Experimental results demonstrate that our algorithm yields more reasonable results and better performance of accuracy than baselines.

Keywords: Complex networks; similarity of nodes; kernel spectral method; link-based similarity (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119400059

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