Task: Summarization
Input (English): 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.
Output (English): This paper introduces loss correction for Graph Neural Networks to deal with symmetric graph label noise, focused on a graph classification task.