  Graph Neural Networks (GNNs) require a relatively large number of labeled
nodes and a reliable/uncorrupted graph connectivity structure in order to
obtain good performance on the semi-supervised node classification task. The
performance of GNNs can degrade significantly as the number of labeled nodes
decreases or the graph connectivity structure is corrupted by adversarial
attacks or due to noises in data measurement /collection. Therefore, it is
important to develop GNN models that are able to achieve good performance when
there is limited supervision knowledge -- a few labeled nodes and noisy graph
structures. In this paper, we propose a novel Dual GNN learning framework to
address this challenge task. The proposed framework has two GNN based node
prediction modules. The primary module uses the input graph structure to induce
regular node embeddings and predictions with a regular GNN baseline, while the
auxiliary module constructs a new graph structure through fine-grained spectral
clusterings and learns new node embeddings and predictions. By integrating the
two modules in a dual GNN learning framework, we perform joint learning in an
end-to-end fashion. This general framework can be applied on many GNN baseline
models. The experimental results validate that the proposed dual GNN framework
can greatly outperform the GNN baseline methods when the labeled nodes are
scarce and the graph connectivity structure is noisy.
