A comprehensive framework for link prediction in multiplex networks

Fengqin Tang (), Cuixia Li, Chungning Wang, Yi Yang and Xuejing Zhao
Additional contact information
Fengqin Tang: Huaibei Normal University
Cuixia Li: Xuzhou University of Technology
Chungning Wang: Lanzhou University of Finance and Economics
Yi Yang: Huaibei Normal University
Xuejing Zhao: Lanzhou University

Computational Statistics, 2024, vol. 39, issue 2, No 22, 939-961

Abstract: Abstract The idea of predicting links in multiplex networks has gained increasing interest in recent years. In this paper, we propose a comprehensive framework which benefits from the structural information of auxiliary layers to predict links on a target layer of multiplex networks. Specifically, we assume that the likelihood of the existence of a link between two nodes is determined by the contributions from both the nodes’ neighbors on the target layer and their counterparts’ neighbors on a manually network generated by auxiliary layers. The final likelihood matrix is acquired by an iterative algorithm. In addition, we show advantages of our methods for predicting links on sparse and dense networks as well as on networks with assortative and disassortative structural layers. The effectiveness of the proposed methods are evaluated through extensive experiments on real-world multiplex networks.

Keywords: Network analysis; Link prediction; Multiplex networks; Interlayer similarity (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-023-01334-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:compst:v:39:y:2024:i:2:d:10.1007_s00180-023-01334-8

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-023-01334-8

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().