Boosting Temporal Graph Learning From Perspectives of Global and Local Structures

Abstract

Learning on temporal graphs has attracted tremendous research interest due to its wide range of applications. Some works intuitively merge graph neural networks (GNNs) and recurrent neural networks (RNNs) to capture structural and temporal information, and recent works propose to aggregate information from neighbor nodes in local subgraphs based on message passing or random walks. These methods produce node embeddings from a global or local perspective and ignore the complementarity between them, thus facing limitations in capturing complex and entangled dynamic patterns when applied to diverse datasets or evaluated by more challenging evaluation protocols. To address the issues, we propose the global and local embedding network (GLEN) for effective and efficient temporal graph representation learning. Specifically, GLEN dynamically generates embeddings for graph nodes by considering both global and local perspectives using specially designed modules. Then, global and local embeddings are combined by a devised cross-perspective fusion module to extract high-order semantic relations of node embeddings. We evaluate GLEN on multiple real-world datasets and apply more stringent evaluation procedures. Extensive experimental results demonstrate that GLEN outperforms other baselines in both link prediction and dynamic node classification tasks. Moreover, with concise and effective modules, GLEN can achieve a better balance between inference precision and training efficiency.