CLNIE: A Contrastive Learning Based Node Importance Evaluation Method for Knowledge Graphs with Few Labels

Abstract

Graph node importance estimation, which evaluates the importance of graph nodes, is an important graph mining problem and has been widely used in many fields such as search engines and recommender systems. Different from ordinary graphs, there are various types of nodes and relationships in knowledge graphs. The relationships in knowledge graphs encode different information, so the information of nodes in a knowledge graph is richer, which leads to the evaluation of the importance of nodes in knowledge graphs being more complicated. The existing research on the importance of nodes in knowledge graphs is mainly based on the assumption that there are enough labels, which even reach 70% of the dataset. However, there are usually few labels in reality. To better study the importance estimation of nodes in a knowledge graph when labels are sparse, we propose a node importance evaluation algorithm based on contrastive learning. First, an unsupervised contrastive loss is designed to generate rich node representations by maximizing the consistency of representations under different views of the same node. To utilize scarce but valuable labeled data for learning node importance, we design a semi-supervised contrastive loss, which solves the problem of failing to determine positive and negative examples in the task of node importance evaluation. In order to improve the effectiveness of contrastive learning, we propose a negative sampling strategy based on label similarity. Negative samples are constructed according to the label difference. Finally, the experimental results on real-world datasets confirm the effectiveness of CLNIE, which achieves a significant performance improvement over the state-of-the-art solutions.