Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning (Extended Abstract)

Recently, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. However, the majority class of early GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more complex nodes and edges in heterogeneous graphs. Also, few heterogeneous GNNs can bypass the transductive learning scheme where all nodes must be known during training, highlighting the need for inductiveness. Furthermore, the training efficiency of most heterogeneous GNNs has been hindered by their sophisticated designs for extracting the semantics associated with each meta path or relation type. In this paper, we propose wide and deep message passing network (WIDEN) to cope with the aforementioned problems about heterogeneity, inductiveness, and efficiency that are rarely investigated together in graph representation learning. We propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes. Meanwhile, we present an innovative downsampling strategy to facilitate faster information propagation. Experiments on real-world heterogeneous graphs have further validated the performance of WIDEN from both effectiveness and efficiency perspectives.