  In recent years, plentiful evidence illustrates that Graph Convolutional
Networks (GCNs) achieve extraordinary accomplishments on the node
classification task. However, GCNs may be vulnerable to adversarial attacks on
label-scarce dynamic graphs. Many existing works aim to strengthen the
robustness of GCNs; for instance, adversarial training is used to shield GCNs
against malicious perturbations. However, these works fail on dynamic graphs
for which label scarcity is a pressing issue. To overcome label scarcity,
self-training attempts to iteratively assign pseudo-labels to highly confident
unlabeled nodes but such attempts may suffer serious degradation under dynamic
graph perturbations. In this paper, we generalize noisy supervision as a kind
of self-supervised learning method and then propose a novel Bayesian
self-supervision model, namely GraphSS, to address the issue. Extensive
experiments demonstrate that GraphSS can not only affirmatively alert the
perturbations on dynamic graphs but also effectively recover the prediction of
a node classifier when the graph is under such perturbations. These two
advantages prove to be generalized over three classic GCNs across five public
graph datasets.
