Published on Sun May 05 2019

Learning Graph Neural Networks with Noisy Labels

Hoang NT, Choong Jun Jin, Tsuyoshi Murata

We study the robustness to symmetric label noise of GNNs training procedures. Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.

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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.