Evidential link prediction by exploiting the applicability of similarity indexes to nodes

Fang, Su-Ping; Li, Longjie; Hu, Binyan; Chen, Xiaoyun

doi:10.1016/j.eswa.2022.118397

Cited by 6 publications

(1 citation statement)

References 61 publications

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“…In addition, complex networks themselves often evolve dynamically over time, and new links are often connected between some nodes. Fortunately, the technique of link prediction in complex networks aims to discover hidden or future links between network nodes, including the prediction of unobserved links, i.e., links that actually exist in a network but have not yet been detected, and the prediction of future links , i.e., links that do not exist in the network at present but should exist or are likely to exist in the future [3][4][5][6][7][8][9] . Link prediction, serving as an abstraction for numerous widespread issues, can be utilized in any system that transforms entities and their relationships into a network representation.…”

Section: Incorporating High-frequency Information Into Edge Convoluti...mentioning

confidence: 99%

Incorporating high-frequency information into edge convolution for link prediction in complex networks

Zhang,

Xu,

Zhu

2024

Sci Rep

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Link prediction in complex networks aims to mine hidden or to-be-generated links between network nodes, which plays a significant role in fields such as the cold start of recommendation systems, knowledge graph completion and biomedical experiments. The existing link prediction models based on graph neural networks, such as graph convolution neural networks, often only learn the low-frequency information reflecting the common characteristics of nodes while ignoring the high-frequency information reflecting the differences between nodes when learning node representation, which makes the corresponding link prediction models show over smoothness and poor performance. Focusing on links in complex networks, this paper proposes an edge convolutional graph neural network EdgeConvHiF that fuses high-frequency node information to achieve the representation learning of links so that link prediction can be realized by implementing the classification of links. EdgeConvHiF can also be employed as a baseline, and extensive experiments on real-world benchmarks validate that EdgeConvHiF not only has high stability but also has more advantages than the existing representative baselines.

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Section: Incorporating High-frequency Information Into Edge Convoluti...mentioning

confidence: 99%

Incorporating high-frequency information into edge convolution for link prediction in complex networks

Zhang,

Xu,

Zhu

2024

Sci Rep

View full text Add to dashboard Cite

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Link prediction in bipartite networks via deep autoencoder-like nonnegative matrix factorization

Yu,

Fu,

Zhao

et al. 2025

Applied Soft Computing

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An improved information volume of mass function based on plausibility transformation method

Zhou,

Li,

Deng

2024

Expert Systems with Applications

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Evidential link prediction by exploiting the applicability of similarity indexes to nodes

Cited by 6 publications

References 61 publications

Incorporating high-frequency information into edge convolution for link prediction in complex networks

Incorporating high-frequency information into edge convolution for link prediction in complex networks

Link prediction in bipartite networks via deep autoencoder-like nonnegative matrix factorization

An improved information volume of mass function based on plausibility transformation method

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