In this paper, we propose the Graph Temporal Edge Aggregation (GTEA) framework for inductive learning on Temporal Interaction Graphs (TIGs). Different from previous works, GTEA models the temporal dynamics of interaction sequences in the continuous-time space and simultaneously takes advantage of both rich node and edge/ interaction attributes in the graph. Concretely, we integrate a sequence model with a time encoder to learn pairwise interactional dynamics between two adjacent nodes.This helps capture complex temporal interactional patterns of a node pair along the history, which generates edge embeddings that can be fed into a GNN backbone. By aggregating features of neighboring nodes and the corresponding edge embeddings, GTEA jointly learns both topological and temporal dependencies of a TIG. In addition, a sparsity-inducing self-attention scheme is incorporated for neighbor aggregation, which highlights more important neighbors and suppresses trivial noises for GTEA. By jointly optimizing the sequence model and the GNN backbone, GTEA learns more comprehensive node representations capturing both temporal and graph structural characteristics. Extensive experiments on five large-scale real-world datasets demonstrate the superiority of GTEA over other inductive models.

翻译：本文提出图时间边聚合（GTEA）框架，用于时间交互图（TIG）上的归纳学习。与先前工作不同，GTEA在连续时间空间中建模交互序列的时间动态，同时充分利用图中丰富的节点和边/交互属性。具体而言，我们将序列模型与时间编码器相结合，以学习两个相邻节点之间的成对交互动态，这有助于捕捉节点对沿历史路径的复杂时间交互模式，从而生成可输入图神经网络（GNN）主干网络的边嵌入。通过聚合邻居节点的特征及对应的边嵌入，GTEA联合学习时间交互图的拓扑依赖和时间依赖。此外，我们引入了一种稀疏诱导自注意力机制用于邻居聚合，该机制能突出更重要的邻居并抑制无关噪声。通过联合优化序列模型和GNN主干网络，GTEA可学习到同时捕捉时间与图结构特征的更全面节点表示。在五个大规模真实数据集上的广泛实验表明，GTEA相较于其他归纳模型具有显著优越性。