IEEE Trans Vis Comput Graph. 2023 Jun;29(6):3024-3038. doi: 10.1109/TVCG.2022.3148107. Epub 2023 May 3.
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.
图神经网络（GNN）旨在将深度学习技术扩展到图数据，并在近年来的图分析任务（例如节点分类）中取得了重大进展。然而，与卷积神经网络（CNN）和循环神经网络（RNN）等其他深度学习网络一样，GNN 的细节对模型开发人员和用户都是隐藏的，其行为就像一个黑盒子。因此，很难诊断 GNN 可能存在的错误。尽管已经有许多关于 CNN 和 RNN 的可视化分析研究，但针对 GNN 的研究却很少。本文通过交互式可视化分析工具 GNNLens 填补了这一研究空白，帮助模型开发人员和用户理解和分析 GNN。具体来说，平行集视图和投影视图使用户能够快速识别和验证错误预测集中的错误模式；图视图和特征矩阵视图提供了对单个节点的详细分析，帮助用户对错误模式形成假设。由于 GNN 联合建模了图结构和节点特征，我们通过比较三个模型（GNN、多层感知机（MLP）和不使用特征的 GNN（GNNWUF））的预测结果，揭示了这两种信息的相对影响。两个案例研究和与领域专家的访谈证明了 GNNLens 在促进对 GNN 模型及其错误的理解方面的有效性。
