Heterogeneous Graph Embedding Based on Edge-aware Neighborhood Convolution

Graph neural networks have shown superior performance in representing graph-structured data. However, directly applying most of existing methods to heterogeneous graph learning tasks may be ill-suited or infeasible, since multiple different types of nodes, edges and attributes challenge the methods originally developed for homogeneous networks. Given this situation, heterogeneous graph embedding recently has gained much research attention. Existing heterogeneous graph embedding methods often adopt random walk-based sampling techniques to model both heterogeneity and rich attribute information. These methods strongly rely on customized sample generation strategies and usually consume a lot of memory. To avoid these restrictions, we present a novel model based on edge-aware neighborhood convolution which is designed to extract features from the neighborhood of different types of nodes. The first and second-order proximity-based objectives are formulated for learning node representations. On the task of link prediction and recommendation, experiments on five heterogeneous graph datasets show the effectiveness of our model.