Dynamic heterogeneous graph representation learning with neighborhood type modeling

Graph representation learning aims to learn the representations of graph structured data in low-dimensional space, and has a wide range of applications in graph analysis tasks. Real-world networks are generally heterogeneous and dynamic, which contain multiple types of nodes and edges, and the graph may evolve at a high speed over time. The complex heterogeneous properties and rapidly evolving graph structures make it difficult to learn high-quality graph representations for dynamic heterogeneous graphs. Currently, studies concentrated on representation learning of temporal heterogeneous networks are insufficient. Existing methods either rely on meta-paths where the embedding quality heavily depending on experts' selection, or use network snapshots where the fine-grained temporal information cannot be captured. In this paper, we propose a novel graph neural network model-node signature based Temporal Heterogeneous Graph Attention Network, termed as THGAT, for learning the representations of dynamic heterogeneous networks. THGAT improves the aggregation way of neighborhood information, and pays attention to the enlightenment of the importance of neighbor nodes by heterogeneous informa-tion and temporal information that cannot be ignored in the network. We also innovatively propose three node signature methods for encoding the heterogeneous information of the nodes and use the time encoding technique suitable for real-time networks to directly represent the temporal information, so as to overcome the limitations of existing methods. We conduct experiments on four real-world datasets, and the results demonstrate that THGAT improves the representation learning quality significantly, in aspects of link prediction, node classification, and node clustering, compared to the state-of-the-art methods. To make the work more complete, we also analyze the applicable scenarios of the three node signature methods through experiments, respectively. (c) 2023 Elsevier B.V. All rights reserved.