用高阶结构提升时序图链接预测，更准且省内存
Higher-order Structure Boosts Link Prediction on Temporal Graphs
- 引入超图表示捕捉群体互动关系
- 动态链接预测性能优于现有方法，内存降低50%
- 适合研究时序网络演化与高效模型设计者
时序图神经网络（TGNNs）在建模和预测时序图结构方面受到越来越多关注。然而，现有TGNNs主要关注成对交互，忽视了真实世界时序图中链接形成与演化的高阶结构。同时，这些模型常存在效率瓶颈，进一步限制其表达能力。为此，我们提出高阶结构时序图神经网络（HTGN），将超图表示引入时序图学习。具体地，我们设计算法识别潜在的高阶结构，增强模型捕捉群体交互的能力；通过将多个边特征聚合为超边表示，HTGN有效降低了训练期间的内存开销。理论上证明了该方法的表达能力提升，并在多个真实世界时序图上通过大量实验验证了其有效性与高效性。结果表明，HTGN在动态链接预测任务上表现更优，相比现有方法内存消耗最多降低50%。
原文摘要 · Abstract (English)
Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise interactions while overlooking higher-order structures that are integral to link formation and evolution in real-world temporal graphs. Meanwhile, these models often suffer from efficiency bottlenecks, further limiting their expressive power. To tackle these challenges, we propose a Higher-order structure Temporal Graph Neural Network, which incorporates hypergraph representations into temporal graph learning. In particular, we develop an algorithm to identify the underlying higher-order structures, enhancing the model's ability to capture the group interactions. Furthermore, by aggregating multiple edge features into hyperedge representations, HTGN effectively reduces memory cost during training. We theoretically demonstrate the enhanced expressiveness of our approach and validate its effectiveness and efficiency through extensive experiments on various real-world temporal graphs. Experimental results show that HTGN achieves superior performance on dynamic link prediction while reducing memory costs by up to 50\% compared to existing methods.
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