  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.
