Asymmetric influence-based superposed random walk link prediction algorithm in complex networks

Shihu Liu (), Xueli Feng and Jin Yang ()
Additional contact information
Shihu Liu: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, P. R. China
Xueli Feng: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, P. R. China
Jin Yang: School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 10, 1-30

Abstract: Random walk-based link prediction algorithms have achieved desirable results for complex network mining, but in these algorithms, the transition probability of particles usually only considers node degrees, resulting in particles being able to randomly select adjacent nodes for random walks in an equal probability manner, to solve this problem, the asymmetric influence-based superposed random walk link prediction algorithm is proposed in this paper. This algorithm encourages particles to choose the next node at each step of the random walk process based on the asymmetric influence between nodes. To this end, we fully consider the topological information around each node and propose the asymmetric influence between nodes. Then, an adjustable parameter is applied to normalize the degree of nodes and the asymmetric influence between nodes into transition probability. Based on this, the proposed new transition probability is applied to superposed random walk process to measure the similarity between all nodes in the network. Empirical experiments are conducted on 16 real-world network datasets such as social network, ecology network, and animal network. The experimental results show that the proposed algorithm has high prediction accuracy in most network, compared with 10 benchmark indices.

Keywords: Complex network; link prediction; asymmetric influence; superposed random walk (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183124420026
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:36:y:2025:i:10:n:s0129183124420026

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183124420026

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().