Multi-View Heterogeneous Graph Attention Network

Heterogeneous Graph Neural Networks (HGNNs) have achieved significant success in various real-world applications. However, existing methods often employ meta-paths, the random walk or shallow homogeneous convolutional graph neural networks such as GCN and GAT to aggregate neigh-borhood information. The meta-path requires prior knowledge of domain experts, while the random walk is not conducive to the link prediction task compared with the direct neighbor method. And the shallow models may have limited capability to capture the information of high-order neighbors. To address these challenges, we propose Multi-View Heterogeneous Graph Attention Network (MHAN), a novel approach that captures the neighbor information from both the local and the global view to effectively learn node representations. In MHAN, we first map the attributes of nodes of different types into the same feature space. Secondly, we utilize the attention mechanism to aggregate information from the first-order and high-order neighbors, respectively. Finally, we generate the final node representations by aggregating the obtained node embeddings from different views. Extensive experiments on three real-world datasets of heterogeneous graphs show that our model outperforms state-of-the-art baselines on the link prediction task.