# GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

arXiv: https://arxiv.org/abs/2011.11048 · HTML: https://mlsanity.com/paper/2011.11048/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

## Built on by (most cited first, 14 of 14)

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- [Rethinking the Trigger-injecting Position in Graph Backdoor Attack](https://mlsanity.com/paper/2304.02277/) (2023)
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- [EL-VIT: Probing Vision Transformer with Interactive Visualization](https://mlsanity.com/paper/2401.12666/) (2024)
- [CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models](https://mlsanity.com/paper/2307.12382/) (2023)
- [Global Concept Explanations for Graphs by Contrastive Learning](https://mlsanity.com/paper/2404.16532/) (2024)
- [Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective](https://mlsanity.com/paper/2309.09744/) (2023)
- [HypoChainer: A Collaborative System Combining LLMs and Knowledge Graphs for Hypothesis-Driven Scientific Discovery](https://mlsanity.com/paper/2507.17209/) (2025)
- [GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks](https://mlsanity.com/paper/2406.04548/) (2024)
- [InteractiveGNNExplainer: A Visual Analytics Framework for Multi-Faceted Understanding and Probing of Graph Neural Network Predictions](https://mlsanity.com/paper/2511.13160/) (2025)
- [Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization](https://mlsanity.com/paper/2512.14181/) (2025)

## Abstract (arXiv)

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.