  Graph Neural Networks (GNNs) have shown their great ability in modeling graph
structured data. However, real-world graphs usually contain structure noises
and have limited labeled nodes. The performance of GNNs would drop
significantly when trained on such graphs, which hinders the adoption of GNNs
on many applications. Thus, it is important to develop noise-resistant GNNs
with limited labeled nodes. However, the work on this is rather limited.
Therefore, we study a novel problem of developing robust GNNs on noisy graphs
with limited labeled nodes. Our analysis shows that both the noisy edges and
limited labeled nodes could harm the message-passing mechanism of GNNs. To
mitigate these issues, we propose a novel framework which adopts the noisy
edges as supervision to learn a denoised and dense graph, which can down-weight
or eliminate noisy edges and facilitate message passing of GNNs to alleviate
the issue of limited labeled nodes. The generated edges are further used to
regularize the predictions of unlabeled nodes with label smoothness to better
train GNNs. Experimental results on real-world datasets demonstrate the
robustness of the proposed framework on noisy graphs with limited labeled
nodes.
