Robust Node Representation Learning via Graph Variational Diffusion
  Networks
Jun Zhuang Mohammad Al Hasan
Node representation learning by using Graph Neural Networks (GNNs) has been
widely explored. However, in recent years, compelling evidence has revealed
that GNN-based node representation learning can be substantially deteriorated
by delicately-crafted perturbations in a graph structure. To learn robust node
representation in the presence of perturbations, various works have been
proposed to safeguard GNNs. Within these existing works, Bayesian label
transition has been proven to be more effective, but this method is extensively
reliant on a well-built prior distribution. The variational inference could
address this limitation by sampling the latent node embedding from a Gaussian
prior distribution. Besides, leveraging the Gaussian distribution (noise) in
hidden layers is an appealing strategy to strengthen the robustness of GNNs.
However, our experiments indicate that such a strategy can cause over-smoothing
issues during node aggregation. In this work, we propose the Graph Variational
Diffusion Network (GVDN), a new node encoder that effectively manipulates
Gaussian noise to safeguard robustness on perturbed graphs while alleviating
over-smoothing issues through two mechanisms: Gaussian diffusion and node
embedding propagation. Thanks to these two mechanisms, our model can generate
robust node embeddings for recovery. Specifically, we design a retraining
mechanism using the generated node embedding to recover the performance of node
classifications in the presence of perturbations. The experiments verify the
effectiveness of our proposed model across six public datasets.