Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. (arXiv:2201.00232v1 [cs.LG])

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 …

arxiv graph graph neural networks graphs networks neural networks