Citation link prediction based on multi-relational neural topic model.

Autor/inn/en	Niu, Fenggao; Zhao, Yating
Titel	Citation link prediction based on multi-relational neural topic model.
Quelle	In: Scientometrics, (2023) 9, S.5277-5292 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-023-04766-7
Schlagwörter	Citation networks; Neural networks; Topic models; Auto-Encoder; Citation link prediction
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.
Erfasst von	OLC
Update	2024/1/01