research-article

Betweenness centrality-based community adaptive network representation for link prediction

Authors:

Mingqiang Zhou,

Qizhi HanAuthors Info & Claims

Applied Intelligence, Volume 52, Issue 4

Pages 3545 - 3558

https://doi.org/10.1007/s10489-021-02633-7

Published: 01 March 2022 Publication History

Abstract

Link prediction is a fundamental problem in biological network analysis, personalized recommendation, network evolution modeling, etc. It aims at discovering links in the network that are unknown, missing, or will be formed in the future. Network representation learning-based link prediction approaches have drawn extensive attention, due to its high efficiency. The previous approaches use random or hyper parameters to select nodes from neighbors or communities when generating walk sequences. However, they do not fully consider the contribution of nodes to the embedded representation and hence impairing the affect the role of community structure in link prediction. To overcome this limitation and utilize community structure, we propose a betweenness centrality-based community adaptive network representation for link prediction method called CALP, which forms network representation by using betweenness centrality to measure the different contribution of community nodes and neighbor nodes for embedding and then applies it to link prediction. CALP first divides the network into communities. Then, it selects a node from the community nodes or neighbor nodes to join the walk sequence by the contribution of the node to embedding. Finally, it generates the corresponding network representation for link prediction. Experiments on realistic networks such as Cora, Citeseer, etc. show that the accuracy of CALP is much better than other approaches.

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Cited By

Li Tzhang RYao YLiu YMa JTang J(2024)Graph regularized autoencoding-inspired non-negative matrix factorization for link prediction in complex networks using clustering information and biased random walkThe Journal of Supercomputing10.1007/s11227-024-06013-z80:10(14433-14469)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11227-024-06013-z
Li LGou FLong HHe KWu J(2022)Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social NetworksWireless Communications & Mobile Computing10.1155/2022/48795572022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4879557

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Published In

cover image Applied Intelligence

Applied Intelligence Volume 52, Issue 4

Mar 2022

1262 pages

ISSN:0924-669X

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2022

Accepted: 18 June 2021

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Cited By

Li Tzhang RYao YLiu YMa JTang J(2024)Graph regularized autoencoding-inspired non-negative matrix factorization for link prediction in complex networks using clustering information and biased random walkThe Journal of Supercomputing10.1007/s11227-024-06013-z80:10(14433-14469)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11227-024-06013-z
Li LGou FLong HHe KWu J(2022)Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social NetworksWireless Communications & Mobile Computing10.1155/2022/48795572022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4879557

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