Learning representations for graph-structured data is essential for graph analytical tasks. While remarkable progress has been made on static graphs, researches on temporal graphs are still in its beginning stage. The bottleneck of the temporal graph representation learning approach is the neighborhood aggregation strategy, based on which graph attributes share and gather information explicitly. Existing neighborhood aggregation strategies fail to capture either the short-term features or the long-term features of temporal graph attributes, leading to unsatisfactory model performance and even poor robustness and domain generality of the representation learning method. To address this problem, we propose a Frame-level Timeline Modeling (FTM) method that helps to capture both short-term and long-term features and thus learns more informative representations on temporal graphs. In particular, we present a novel link-based framing technique to preserve the short-term features and then incorporate a timeline aggregator module to capture the intrinsic dynamics of graph evolution as long-term features. Our method can be easily assembled with most temporal GNNs. Extensive experiments on common datasets show that our method brings great improvements to the capability, robustness, and domain generality of backbone methods in downstream tasks. Our code can be found at https://github.com/yeeeqichen/FTM.

翻译：图结构化数据的表示学习对图分析任务至关重要。尽管静态图研究已取得显著进展，但时序图的研究仍处于起步阶段。时序图表示学习方法的核心瓶颈在于邻域聚合策略——图属性通过该策略显式共享和汇集信息。现有邻域聚合策略无法同时捕捉时序图属性的短期特征与长期特征，导致模型性能不佳，甚至削弱表示学习方法的鲁棒性与领域泛化能力。针对该问题，本文提出一种帧级时间轴建模（FTM）方法，通过同时捕获短期与长期特征，在时序图上学习更具信息量的表示。具体而言，我们引入一种新颖的基于链接的帧分割技术以保留短期特征，并设计时间轴聚合模块来捕捉图演化过程的固有动态特性作为长期特征。该方法可便捷地集成至多数时序GNN中。在通用数据集上的大量实验表明，本文方法显著提升了主干方法在下游任务中的能力、鲁棒性及领域泛化能力。相关代码参见https://github.com/yeeeqichen/FTM。