Diverse Transformation-Augmented Graph Tensor Convolutional Network for Dynamic Graph Representation Learning

A dynamic graphs (DG) is frequently adopted to describe the evolving interactions between nodes in real-world applications such as device communication networks. Temporal patterns are the natural characteristics of DG and are also the key to representation learning. However, most of the existing dynamic GCN models consist of static GCN and sequence modules, resulting in the separation of spatiotemporal information and the inability to effectively capture the complex temporal patterns in DG. To solve this problem, this study proposes a Diverse Transformation-Augmented Graph Tensor Convolutional Network (DTGTCN) with three-fold ideas: a) leveraging the tensor M-product to formulate the unified graph tensor convolution network (GTCN) without separate representation of spatiotemporal information; b) introducing three transformation schemes into GTCN to model complex temporal patterns for aggregating temporal information; c) building the ensemble of diverse transformation schemes to obtain high representation capacity. Empirical studies on four DGs emerging from communication networks demonstrate that owing to diverse transformation, the proposed DTGTCN significantly outperforms state-of-the-art models in addressing the task of link weight estimation.