Temporal Adaptive Aggregation Network for Dynamic Graph Learning

Abstract

Dynamic graphs are common in many applications, such as social networks with evolving nodes and edges over time. When handling such dynamics, existing approaches typically suffer from two limitations: (1) they primarily focus on network topology, without taking node class connections and temporal changes into consideration; and (2) the learning objective is primarily constrained by labeled nodes, which often result in over-smoothing and weak-generalization in representation learning, because labeled nodes are limited. In this paper, we propose a temporal adaptive aggregation network (TAAN) for dynamic graph learning. We consider a dynamic graph as a network with changing nodes and edges in temporal order. The temporal adaptive aggregation is to ensure that, for each node, the information aggregation is to consider neighbors from different classes, as well as their temporal order. For each snapshot of the dynamic network, data augmentation and consistency loss are combined to leverage labeled and unlabeled nodes to learn good node embedding. Meanwhile, in order to accommodate temporal changes of graphs, an incremental learning process is used to ensure that learning on each snapshot can inherit weights learned from previous time points, so graph learning can adapt to the dynamic graph environments. Experiments on real-world datasets validate the effectiveness of our approach.