Learning Graph Neural Networks with Noisy Labels

Hoang NT, Jun Jin Choong, Tsuyoshi Murata

Mar 20, 2019 ICLR 2019 Workshop LLD Blind Submission readers: everyone
  • Keywords: weakly supervised learning, noisy label data, graph neural network, loss correction
  • TL;DR: We apply loss correction to graph neural networks to train a more robust to noise model.
  • Abstract: 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.
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