mlsanityData to 7 Oct 2026

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis

arXiv 2107.05097 ↗First posted 11 Jul 2021cs.LG cs.CV eess.IVcited by 13

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Abstract (arXiv)

Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience. GNNs are promising to model complicated network data, but they are prone to overfitting and suffer from poor interpretability, which prevents their usage in decision-critical scenarios like healthcare. To bridge this gap, we propose BrainNNExplainer, an interpretable GNN framework for brain network analysis. It is mainly composed of two jointly learned modules: a backbone prediction model that is specifically designed for brain networks and an explanation generator that highlights disease-specific prominent brain network connections. Extensive experimental results with visualizations on two challenging disease prediction datasets demonstrate the unique interpretability and outstanding performance of BrainNNExplainer.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 11 Jul 2021.

Builds on

21 of 47 references.

References · 21

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9 more
  1. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  2. 2018Invariant and Equivariant Graph Networkscited by 339
  3. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 210
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Citing papers per year: 2022: 8; 2023: 2; 2024: 1; 2025: 2.

13 papers.

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  1. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 73
  2. 2022BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneckcited by 17
  3. 2023CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosiscited by 16
  4. 2022Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learningcited by 15
  5. 2022Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosiscited by 11
  6. 2023Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networkscited by 11
  7. 2022Principle of Relevant Information for Graph Sparsificationcited by 9
  8. 2022Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Modelcited by 8
  9. 2022Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Predictioncited by 6
  10. 2022HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networkscited by 2
  11. 2025Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progressioncited by 1
  12. 2024Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion Prediction with T1-MRI-based Brain Networkcited by 0
1 more
  1. 2025ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule miningcited by 0