HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks
Guanghui Zhu Zhennan Zhu Hongyang Chen Chunfeng Yuan Yihua Huang
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 simple homogeneous graph
model without meta-path can also achieve comparable results, which calls into
question the necessity of meta-path. In this paper, we first present the
intrinsic difference about 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 semantic information in heterogeneous graphs
comprehensively, namely HAGNN (Hybrid Aggregation for Heterogeneous GNNs). 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 intra-type aggregation and
meta-path-free inter-type aggregation. During the intra-type aggregation phase,
we propose a new data structure called 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 of HAGNN.