Structure-Aware Model for Representation Learning on Temporal Graphs

Temporal graph representation learning seeks to capture the intrinsic evolution of nodes in temporal graphs for various applications. While existing models primarily learn node representations by aggregating temporal information from historical interactions of nodes, they often overlook the critical structural impacts arising from these interactions. To address this issue, we propose a Structure-aware model for Temporal Graph representation learning (STG), a framework that explicitly incorporates the impacts of evolving structural roles to enhance the learned node representations. Specifically, STG encodes distinct structural roles of nodes by extracting both single-unit and multi-unit interaction patterns. These roles are then transformed into the Fourier domain for a deeper analysis of the complex structural dynamics. To capture the structural impacts on future node interactions, we design a dynamic filter to process these roles. The filter is equipped with a personalized weight coefficient generator to perform the interaction-specific analysis. Finally, we employ a mixer to collaboratively aggregate the temporal and structural information to obtain structure-aware temporal node representations. Extensive experiments conducted on several real-world temporal graph datasets demonstrate the superior performance of our model in dynamic link prediction tasks under both transductive and inductive settings.