  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 RNNs, little
research has addressed the challenges for GNNs. This paper fills the research
gap with an interactive visual analysis tool, GNNLens, to assist model
developers and users in understanding and analyzing GNNs. Specifically,
Parallel Sets View and Projection View enable users to quickly identify and
validate error patterns in the set of wrong predictions; Graph View and Feature
Matrix View offer a detailed analysis of individual nodes to assist users in
forming hypotheses about the error patterns. Since GNNs jointly model the graph
structure and the node features, we reveal the relative influences of the two
types of information by comparing the predictions of three models: GNN,
Multi-Layer Perceptron (MLP), and GNN Without Using Features (GNNWUF). Two case
studies and interviews with domain experts demonstrate the effectiveness of
GNNLens in facilitating the understanding of GNN models and their errors.
