PGRA: Projected graph relation-feature attention network for heterogeneous information network embedding

Graph neural networks (GNNs) have achieved superior performance and gained significant interest in various domains. However, most of the existing GNNs are considered for homogeneous graphs, whereas real-world systems are usually modeled as heterogeneous graphs or heterogeneous information networks (HINs). Designing a GNN to fully capture the rich semantic information of HINs is significantly challenging owing to the heterogeneity and incompatibility of relations in HINs. To address these issues while utilizing the power of GNNs, we propose a novel unsupervised embedding approach, named Projected Graph Relation-Feature Attention Network (PGRA). PGRA is based on three mechanisms: 1) specific-relation projection that projects the representation vector of each node to a relation-specific space, 2) aggregation with a relation-feature attention network that learns salient neighbors in the aggregation by considering the features of the nodes and compatibility between the connected and target relations, 3) an elegantly designed loss function that captures both the first-and second-order proximities between nodes. The results of extensive experiments on seven real-world datasets illustrate that PGRA outperforms the state-of-the-art methods by a large margin. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).