# Explainable Classification of Brain Networks via Contrast Subgraphs

arXiv: https://arxiv.org/abs/2006.05176 · HTML: https://mlsanity.com/paper/2006.05176/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 26 of 26)

- [Towards Self-Interpretable Graph-Level Anomaly Detection](https://mlsanity.com/paper/2310.16520/) (2023)
- [Counterfactual Graphs for Explainable Classification of Brain Networks](https://mlsanity.com/paper/2106.08640/) (2021)
- [A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges](https://mlsanity.com/paper/2210.12089/) (2022)
- [A Comprehensive Survey on Self-Interpretable Neural Networks](https://mlsanity.com/paper/2501.15638/) (2025)
- [Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification](https://mlsanity.com/paper/2409.10944/) (2024)
- [Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy](https://mlsanity.com/paper/2410.20366/) (2024)
- [Data-Driven Network Neuroscience: On Data Collection and Benchmark](https://mlsanity.com/paper/2211.12421/) (2022)
- [Deep Orthogonal Hypersphere Compression for Anomaly Detection](https://mlsanity.com/paper/2302.06430/) (2023)
- [A Survey on the Densest Subgraph Problem and Its Variants](https://mlsanity.com/paper/2303.14467/) (2023)
- [BrainOOD: Out-of-distribution Generalizable Brain Network Analysis](https://mlsanity.com/paper/2502.01688/) (2025)
- [GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs](https://mlsanity.com/paper/2202.08815/) (2022)
- [GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation](https://mlsanity.com/paper/2206.02957/) (2022)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion](https://mlsanity.com/paper/2410.08228/) (2024)
- [The Generalized Mean Densest Subgraph Problem](https://mlsanity.com/paper/2106.00909/) (2021)
- [Self-learn to Explain Siamese Networks Robustly](https://mlsanity.com/paper/2109.07371/) (2021)
- [CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks](https://mlsanity.com/paper/2210.08811/) (2022)
- [Contrastive Graph Pooling for Explainable Classification of Brain Networks](https://mlsanity.com/paper/2307.11133/) (2023)
- [Counterfactual Explanations for Graph Classification Through the Lenses of Density](https://mlsanity.com/paper/2307.14849/) (2023)
- [Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection](https://mlsanity.com/paper/2505.21285/) (2025)
- [Differentially Describing Groups of Graphs](https://mlsanity.com/paper/2201.04064/) (2021)
- [Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective](https://mlsanity.com/paper/2505.18002/) (2025)
- [Graph-level Neural Networks: Current Progress and Future Directions](https://mlsanity.com/paper/2205.15555/) (2022)
- [TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis](https://mlsanity.com/paper/2309.07947/) (2023)
- [Faster Algorithms for Generalized Mean Densest Subgraph Problem](https://mlsanity.com/paper/2310.11377/) (2023)

## Abstract (arXiv)

Mining human-brain networks to discover patterns that can be used to discriminate between healthy individuals and patients affected by some neurological disorder, is a fundamental task in neuroscience. Learning simple and interpretable models is as important as mere classification accuracy. In this paper we introduce a novel approach for classifying brain networks based on extracting contrast subgraphs, i.e., a set of vertices whose induced subgraphs are dense in one class of graphs and sparse in the other. We formally define the problem and present an algorithmic solution for extracting contrast subgraphs. We then apply our method to a brain-network dataset consisting of children affected by Autism Spectrum Disorder and children Typically Developed. Our analysis confirms the interestingness of the discovered patterns, which match background knowledge in the neuroscience literature. Further analysis on other classification tasks confirm the simplicity, soundness, and high explainability of our proposal, which also exhibits superior classification accuracy, to more complex state-of-the-art methods.