Learning Graph Neural Networks with Noisy Labels

5 May 2019 Hoang NT Choong Jun Jin Tsuyoshi Murata

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.).. (read more)

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