  Node classification for graph-structured data aims to classify nodes whose
labels are unknown. While studies on static graphs are prevalent, few studies
have focused on dynamic graph node classification. Node classification on
dynamic graphs is challenging for two reasons. First, the model needs to
capture both structural and temporal information, particularly on dynamic
graphs with a long history and require large receptive fields. Second, model
scalability becomes a significant concern as the size of the dynamic graph
increases. To address these problems, we propose the Time Augmented Dynamic
Graph Neural Network (TADGNN) framework. TADGNN consists of two modules: 1) a
time augmentation module that captures the temporal evolution of nodes across
time structurally, creating a time-augmented spatio-temporal graph, and 2) an
information propagation module that learns the dynamic representations for each
node across time using the constructed time-augmented graph. We perform node
classification experiments on four dynamic graph benchmarks. Experimental
results demonstrate that TADGNN framework outperforms several static and
dynamic state-of-the-art (SOTA) GNN models while demonstrating superior
scalability. We also conduct theoretical and empirical analyses to validate the
efficiency of the proposed method. Our code is available at
https://sites.google.com/view/tadgnn.
