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:时间图学习旨在为基于图表的任务生成高质量的展示,同时提供动态信息,而动态信息最近引起越来越多的注意。与静态图表不同,时间图通常以连续时间而不是相邻矩阵的节点互动序列的形式组织。大多数时间图学习方法通过合并历史信息来模拟当前互动。然而,这些方法仅仅考虑第一阶时间信息,而忽视重要的高阶结构信息,导致低端性能。为了解决这一问题,我们通过提取时间和结构信息来学习信息,学习更多的信息节点表达方式。我们提出了一种自我监督的方法,称为S2T,用于时间图学习。注意,第一阶时间信息和高阶结构信息以不同的方式组合在一起,最初节点显示可以分别计算两个条件的强度。随后引入了调整损失,以优化节点表达方式,通过缩小两种强度之间的差距来提供更丰富的信息。具体地说,除了利用历史相邻序列来模拟时间信息外,我们还进一步考虑从当地和全球两级的结构性信息。在最接近性图学级别上,没有根据不同的结构强度,我们用不同的结构序列生成了不同的结构模型,在10级上,我们根据不同的结构序列生成了不同的结构模型进行结构模型。