NLA-GNN: Non-local information aggregated graph neural network for heterogeneous graph embedding

Abstract

Heterogeneous graphs are ubiquitous in the real world. Recent methods aim to obtain meaningful low-dimensional node embeddings from heterogeneous graphs to facilitate downstream applications. However, most existing methods tend to consider the local information but ignore the non-local information. This paper proposes a novel Non-Local Information Aggregated Graph Neural Network (NLA-GNN) that aggregate not only the local information from neighbor nodes but also non-local information from distant nodes. Specifically, Local aggregation modules in NLA-GNN utilize the attention mechanism to generate potentially valuable metapaths and use them to aggregate local information. In contrast, non-local aggregation modules adopt a two-step approach, and each step uses an attention-guided method to sort nodes into node sequences and aggregate information with the methods designed for sequential data. Experiment results on three heterogeneous graph datasets demonstrate the performance of NLA-GNN over state-of-the-arts and the necessity of non-local aggregation in heterogeneous graphs.