Learning Graph Neural Networks with Noisy Labels
Hoang Nt, Choong Jun Jin, Tsuyoshi Murata · arXiv (Cornell University) · 2019
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.