关键词: 图神经网络, 动态表征, 邻域聚合, 节点分类, 链接预测

Abstract:

Dynamic graph learning addresses graph data in which nodes and edges evolve over time. Traditional Graph Neural Networks （GNNs） typically rely on information from nodes and their neighbors， but in dynamic graph scenarios， models must capture real-time dynamic evolution information of nodes and edges while balancing global temporal dependencies and local structural details when facing large-scale data. To address these challenges， MTGN （Memory-based Temporal Graph Network）， a scalable dynamic graph representation learning model with neighborhood memory was proposed to realize efficient modeling via subgraph sampling and neighborhood memory mechanism. First， a parallel temporal sampler was used for efficient subgraph sampling. Second， a cache-based node neighborhood memory mechanism was introduced to maintain node historical status dynamically. Third， temporal relationships among dynamic neighbors were modeled through an attention mechanism adaptively， thereby generating more stable and robust dynamic graph representations. Finally， experiments were conducted on four real-world datasets. The results demonstrate that MTGN has excellent prediction accuracy in the link prediction tasks with the prediction accuracies of 99.38% and 74.46% on the MOOC and LASTFM datasets， respectively. In the dynamic node classification tasks， MTGN achieves prediction accuracies of 90.50% and 79.08% on the Wikipedia and Reddit datasets， respectively， showing significant performance improvements compared to the baseline methods. Additionally， MTGN has outstanding training efficiency， reducing training time by at least 86.9% and 93.6% on the Wikipedia and Reddit datasets， respectively， compared to the baseline methods， validating its scalability for large-scale dynamic graph tasks.

Key words: Graph Neural Network (GNN), dynamic representation, neighborhood aggregation, node classification, link prediction