Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs

Jiaren Xiao, Quanyu Dai, Xiaochen Xie, James Lam, Ka-Wai Kwok

Graph embedding is a general approach to tackling graph-analytic problems by encoding nodes into low-dimensional representations. Most existing embedding methods are transductive since the information of all nodes is required in training, including those to be predicted. In this paper, we propose a novel inductive embedding method for semi-supervised learning on graphs. This method generates node representations by learning a parametric function to aggregate information from the neighborhood using an attention mechanism, and hence naturally generalizes to previously unseen nodes. Furthermore, adversarial training serves as an external regularization enforcing the learned representations to match a prior distribution for improving robustness and generalization ability. Experiments on real-world clean or noisy graphs are used to demonstrate the effectiveness of this approach.

Knowledge Graph

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