DAHGN: Degree-Aware Heterogeneous Graph Neural Network

Abstract

In recent years, Graph Neural Networks (GNNs), an emerging technology for learning from graph-structured data, have attracted much attention. Despite the widespread applications of GNNs across diverse domains, recent studies have shown that insufficient information aggregation for low-degree nodes (i.e., tail nodes) leads to inferior performance of GNNs. While numerous recent studies have aimed to address the issue of degree bias in homogeneous graphs, there are limited studies on analyzing such unfairness for heterogeneous information networks. In this article, we propose a novel semi-supervised contrastive learning framework named Degree-Aware Heterogeneous Graph Neural Network (DAHGN), which achieves promising performance by alleviating the degree bias. In brief, we first construct two contrastive graph views, one from a heterogeneous graph that focuses on capturing complementary information to enhance the node representations and one from an extracted homogeneous subgraph that focuses on applying different strategies to low- and high- degree nodes to eliminate the degree bias. We then leverage the label learned from the heterogeneous view to reconstruct the positive node pairs in the contrastive learning framework. Finally, we combine the task loss (semi-supervised) and the contrastive loss to optimize the model performance. We conduct extensive experiments on benchmark datasets to validate the effectiveness of our model, and the results demonstrate that the DAHGN outperforms a wide range of state-of-the-art methods in the semi-supervised node classification problem.