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An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers

arXiv 2203.16751 ↗First posted 31 Mar 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

Learning knowledge representation of scientific paper data is a problem to be solved, and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this problem. This paper proposes an unsupervised cluster-level scientific paper heterogeneous graph node representation learning method (UCHL), aiming at obtaining the representation of nodes (authors, institutions, papers, etc.) in the heterogeneous graph of scientific papers. Based on the heterogeneous graph representation, this paper performs link prediction on the entire heterogeneous graph and obtains the relationship between the edges of the nodes, that is, the relationship between papers and papers. Experiments results show that the proposed method achieves excellent performance on multiple evaluation metrics on real scientific paper datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 31 Mar 2022.

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References · 24

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12 more
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