HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks

Abstract

Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent work pointed out that a simple homogeneous graph model without a meta-path can also achieve comparable results, which calls into question the necessity of a meta-path. In this article, we first present the intrinsic difference between meta-path-based and meta-path-free models, i.e., how to select neighbors for node aggregation. Then, we propose a novel framework to utilize the rich type of semantic information in heterogeneous graphs comprehensively, namely, hybrid aggregation for heterogeneous GNNs (HAGNNs). The core of HAGNN is to leverage the meta-path neighbors and the directly connected neighbors simultaneously for node aggregations. HAGNN divides the overall aggregation process into two phases: meta-path-based intratype aggregation and meta-path-free intertype aggregation. During the intratype aggregation phase, we propose a new data structure called a fused meta-path graph and perform structural semantic aware aggregation on it. Finally, we combine the embeddings generated by each phase. Compared with existing heterogeneous GNN models, HAGNN can take full advantage of the heterogeneity in heterogeneous graphs. Extensive experimental results on node classification, node clustering, and link prediction tasks show that HAGNN outperforms the existing modes, demonstrating the effectiveness and efficiency of HAGNN.