Temporal graph learning aims to generate high-quality representations for graph-based tasks along with dynamic information, which has recently drawn increasing attention. Unlike the static graph, a temporal graph is usually organized in the form of node interaction sequences over continuous time instead of an adjacency matrix. Most temporal graph learning methods model current interactions by combining historical information over time. However, such methods merely consider the first-order temporal information while ignoring the important high-order structural information, leading to sub-optimal performance. To solve this issue, by extracting both temporal and structural information to learn more informative node representations, we propose a self-supervised method termed S2T for temporal graph learning. Note that the first-order temporal information and the high-order structural information are combined in different ways by the initial node representations to calculate two conditional intensities, respectively. Then the alignment loss is introduced to optimize the node representations to be more informative by narrowing the gap between the two intensities. Concretely, besides modeling temporal information using historical neighbor sequences, we further consider the structural information from both local and global levels. At the local level, we generate structural intensity by aggregating features from the high-order neighbor sequences. At the global level, a global representation is generated based on all nodes to adjust the structural intensity according to the active statuses on different nodes. Extensive experiments demonstrate that the proposed method S2T achieves at most 10.13% performance improvement compared with the state-of-the-art competitors on several datasets.

翻译：时序图学习旨在结合动态信息为基于图的任务生成高质量表示，近年来受到越来越多的关注。与静态图不同，时序图通常以连续时间节点交互序列而非邻接矩阵的形式组织。现有大多数时序图学习方法通过整合历史时序信息建模当前交互。然而，这类方法仅考虑一阶时序信息而忽略高阶结构信息，导致性能次优。针对该问题，通过同时提取时序和结构信息以学习更具信息量的节点表示，我们提出了一种名为S2T的自监督时序图学习方法。具体而言，初始节点表示分别通过不同方式融合一阶时序信息与高阶结构信息以计算两种条件强度，随后引入对齐损失函数，通过缩小两种强度之间的差距来优化节点表示，使其更具信息量。在具体实现中，除利用历史邻居序列建模时序信息外，我们进一步从局部和全局层面考虑结构信息。局部层面，通过聚合高阶邻居序列特征生成结构强度；全局层面，基于所有节点生成全局表示，根据各节点活跃状态调整结构强度。大量实验表明，在多个数据集上，所提方法S2T相比最优基线方法性能提升最高可达10.13%。