论文标题
科学论文中的异质图的学习节点表示的基于群集级的方法
An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers
论文作者
论文摘要
学习科学纸数据的学习知识表示是要解决的问题，如何学习科学纸节点在异质网络中的纸张节点的表示是解决此问题的核心。本文提出了一种无监督的集群级科学论文异质图节点表示方法（UCHL），目的是在科学论文的异质图中获得节点（作者，机构，论文等）的表示。基于异构图表示，本文对整个异质图执行了链接预测，并获得了节点边缘之间的关系，即论文与论文之间的关系。实验结果表明，所提出的方法在实际科学纸数据集的多个评估指标上实现了出色的性能。
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.
