Citation link prediction based on multi-relational neural topic model

Fenggao Niu () and Yating Zhao ()
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Fenggao Niu: Shanxi University
Yating Zhao: Shanxi University

Scientometrics, 2023, vol. 128, issue 9, No 14, 5277-5292

Abstract: Abstract The existing topic models usually focus the citation relationships when considering the relationships between documents. To make full use of the information contained in scientific documents, this paper proposes a Multi-Relational Neural Topic Model (MRNTM) based on the three relationships networks between documents. This work comprehensively considers the multiple relationships networks between documents: citation relationship, author relationship, and co-citation relationship, and then proposes a model that combines Variational Auto-Encoder (VAE) with neural networks multi-layer perceptron (MLP). This model not only can be used to learn more representative document topics, but also capture the complex interaction between documents according to their latent topics. The interaction between documents can further promote topic learning. Experiments on two real datasets show that our model can effectively utilize latent topics and the relationship between document networks, and superior to existing models in topic learning and citation link prediction.

Keywords: Citation networks; Neural networks; Topic models; Auto-Encoder; Citation link prediction (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11192-023-04766-7

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