MTT-DynGL: Towards Multidimensional Topology-oriented Time-series Dynamic Graphs Learning Model

Dynamic graph learning has received increasing attention in recent years. However, real-world graph data sets are characterized by significant structural complexity, attribute diversity, and temporal variability. Importantly, there are complex and significant influence mechanisms between them. All them pose great challenges to dynamic graph learning (DGL). To address them, we propose a novel dynamic graph learning framework, MTT-DynGL. First, graph attention networks (GAT) is used to efficiently aggregate the topology and multidimensional attribute features on each snapshot. Then, a temporal variation matrix with strength factors is designed to further measure the interaction mechanism between structures and attributes over time. Further, to effectively integrate the above results, a MTT-based dynamic graph learning network is designed. It consists of an MTT integration mechanism and a bidirectional dilated causal convolution network. The former is used to learn temporal variation features in an integrated manner, and the latter is used to improve learning quality and training efficiency. Finally, the effectiveness of our method is verified by multiple experiments.