We study the robustness to symmetric label noise of GNNs training procedures. By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task. Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.
We apply loss correction to graph neural networks to train a more robust to noise model. This paper introduces loss correction for Graph Neural Networks to deal with symmetric graph label noise, focused on a graph classification task. This paper proposes the use of a noise correction loss in the context of graph neural networks to deal with noisy labels.