  The high cost of data labeling often results in node label shortage in real
applications. To improve node classification accuracy, graph-based
semi-supervised learning leverages the ample unlabeled nodes to train together
with the scarce available labeled nodes. However, most existing methods require
the information of all nodes, including those to be predicted, during model
training, which is not practical for dynamic graphs with newly added nodes. To
address this issue, an adversarially regularized graph attention model is
proposed to classify newly added nodes in a partially labeled graph. An
attention-based aggregator is designed to generate the representation of a node
by aggregating information from its neighboring nodes, thus naturally
generalizing to previously unseen nodes. In addition, adversarial training is
employed to improve the model's robustness and generalization ability by
enforcing node representations to match a prior distribution. Experiments on
real-world datasets demonstrate the effectiveness of the proposed method in
comparison with the state-of-the-art methods. The code is available at
https://github.com/JiarenX/AGAIN.
