Mohammad Al Hasan

Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-Supervision

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…