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.

In recent years, graph embedding techniques have exhibited great potential for various downstream tasks, which can leverage both topological structures and the temporal dependencies of nodes in their representations, leading to remarkable achievements. However, the multi-role nature of nodes during their temporally interacting is neglected. To tackle this problem, we propose a novel model, Temporal graph Multi-Aspect Embedding (TMAE), to capture the latent multi-aspect characteristics of nodes in temporal graphs, thereby enhancing the quality of graph embeddings. Specifically, we propose to learn the aspect embeddings of nodes and their weights at different timestamps separately for a better adaptation. In contrast to the conventional fixed aspect number assumption, a Hierarchical Dirichlet Process-based approach is employed to dynamically determine the weight of aspects for nodes at different times. Through this framework, we effectively learn the multi-aspect information through Time-reversed Temporal Walks (TTWs). Extensive experiments performed across eight publicly accessible datasets have demonstrated the significant improvements of the proposed TMAE model over state-of-the-art algorithms by taking advantage of the multi-aspect nature.

Graph embedding is regarded as one of the most advanced techniques for graph data analyses due to its significant performance. However, the majority of existing works only focus on static graphs while ignoring the ubiquitous dynamic graphs. In fact, the temporal evolution of edges in a dynamic graph sets a harsh challenge for the traditional embedding algorithms. To solve the problem, in this paper we propose a Dynamic Graph Multi-Aspect Embedding (DGMAE) to automatically learn the proper number of aspects and their distributions in each temporal duration based on a distance dependent Chinese Restaurant Process. The proposed method can encode the inherent property of varying interactions among nodes along the time and present different aspect-influences to nodes embedding. Our extensive experiments on several public datasets show the performance improvement over state-of-the-art works.