GraphTAN: Temporal Attention Network for Learning Graph-Level Embedding

Numerous significant temporal graph tasks, such as graph similarity ranking, trend analysis and anomaly detection, necessitate low-dimensional and high-order graph-level embedding in terms of the evolving nodes and topologies over time. However, most existing graph embedding methods focus on extracting node-level embeddings, while ignoring these evolutions. Therefore, these methods inadequately consider the impact of trends on the overall graph. Moreover, there are a large number of nodes in temporal graphs, which make it difficult to generate effective graph embeddings directly by aggregating dynamic node embeddings. In this study, we propose a novel temporal attention network for learning graph-level embedding learning called GraphTAN. Specifically, the proposed model employs pooling attention to select crucial nodes and filter out noisy ones for each snapshot, thereby enhancing the quality of aggregated graph embeddings. Furthermore, we design a graph-level temporal attention mechanism to effectively extract temporal graph embeddings, capturing trends and patterns across snapshots. The proposed model is evaluated on three downstream tasks. Experimental results demonstrate that GraphTAN captures both the topology structure and fine-grained trend effectively, outperforming the state-of-the-art methods with big margins on multiple tasks over several real networks.