通过局部拓扑增强时序图网络，提升稀疏动态图的链接预测准确率。
A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
- 在候选链接周围提取封闭子图，融合结构与时间信息
- 在稀疏通话记录数据上平均精度提升至少2%
- 适合处理瞬时交互的动态网络，如通信日志
预测稀疏、持续演化的网络中的链接是网络科学的核心挑战。传统启发式方法和深度学习模型（包括图神经网络）通常针对静态图设计，难以捕捉时间依赖性。基于快照的技术虽部分解决此问题，但在具有瞬时交互的网络（如电信通话详单记录）中常面临数据稀疏和类别不平衡问题。时序图网络（TGN）通过随时间更新节点嵌入来建模动态图，但在稀疏条件下的预测精度仍有限。本研究改进TGN框架，通过在候选链接周围提取封闭子图，使模型能够联合学习结构与时间信息。在稀疏通话记录、邮件和消息数据集上的实验表明，该方法相比标准TGN将平均精度提升至少2%，证明了引入局部拓扑对动态网络中鲁棒链接预测的优势。
原文摘要 · Abstract (English)
Predicting links in sparse, continuously evolving networks is a central challenge in network science. Conventional heuristic methods and deep learning models, including Graph Neural Networks (GNNs), are typically designed for static graphs and thus struggle to capture temporal dependencies. Snapshot-based techniques partially address this issue but often encounter data sparsity and class imbalance, particularly in networks with transient interactions such as telecommunication call detail records (CDRs). Temporal Graph Networks (TGNs) model dynamic graphs by updating node embeddings over time; however, their predictive accuracy under sparse conditions remains limited. In this study, we improve the TGN framework by extracting enclosing subgraphs around candidate links, enabling the model to jointly learn structural and temporal information. Experiments on a sparse CDR, email, message dataset show that our approach increases average precision by at least 2% over standard TGNs, demonstrating the advantages of integrating local topology for robust link prediction in dynamic networks.
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