GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks. (arXiv:2011.11048v6 [cs.HC] UPDATED)

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph
data and have achieved significant progress in graph analysis tasks (e.g., node
classification) in recent years. However, similar to other deep neural networks
like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs),
GNNs behave like a black box with their details hidden from model developers
and users. It is therefore difficult to diagnose possible errors of GNNs.
Despite many visual analytics studies being done on CNNs and …

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