Exploiting Interaction Links for Node Classification
with Deep Graph Neural Networks
Abstract
Node classification is an important problem in relational machine learning. However, in scenarios
where graph edges represent interactions among
the entities (e.g., over time), the majority of current methods either summarize the interaction information into link weights or aggregate the links
to produce a static graph. In this paper, we propose
a neural network architecture that jointly captures
both temporal and static interaction patterns, which
we call Temporal-Static-Graph-Net (TSGNet). Our
key insight is that leveraging both a static neighbor encoder, which can learn aggregate neighbor
patterns, and a graph neural network-based recurrent unit, which can capture complex interaction
patterns, improve the performance of node classification. In our experiments on node classification tasks, TSGNet produces significant gains compared to state-of-the-art methods—reducing classification error up to 24% and an average of 10%
compared to the best competitor on four real-world
networks and one synthetic dataset.