# Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

arXiv: https://arxiv.org/abs/2207.07650 · HTML: https://mlsanity.com/paper/2207.07650/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 117 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 30 of 41)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020)
- [Data Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2006.06830/) (2020)
- [Adaptive Sampling Towards Fast Graph Representation Learning](https://mlsanity.com/paper/1809.05343/) (2018)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [InfoGCL: Information-Aware Graph Contrastive Learning](https://mlsanity.com/paper/2110.15438/) (2021)
- [Hierarchical Graph Pooling with Structure Learning](https://mlsanity.com/paper/1911.05954/) (2019)
- [Federated Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2204.10983/) (2022)
- [Fast OSCAR and OWL Regression via Safe Screening Rules](https://mlsanity.com/paper/2006.16433/) (2020)
- [Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity](https://mlsanity.com/paper/1806.01764/) (2018)

## Built on by (most cited first, 8 of 8)

- [Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation](https://mlsanity.com/paper/2405.13190/) (2024)
- [BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis](https://mlsanity.com/paper/2606.29200/) (2026)
- [Whole-Graph Representation Learning For the Classification of Signed Networks](https://mlsanity.com/paper/2409.20073/) (2024)
- [A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases](https://mlsanity.com/paper/2412.06212/) (2024)
- [Coarse-to-Fine Contrastive Learning on Graphs](https://mlsanity.com/paper/2212.06423/) (2022)
- [Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks](https://mlsanity.com/paper/2509.10524/) (2025)
- [Brain PathoGraph Learning](https://mlsanity.com/paper/2509.21742/) (2025)
- [Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum](https://mlsanity.com/paper/2608.19436/) (2026)

## Abstract (arXiv)

Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. Firstly, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes predictions. Moreover, few of current graph learning model is interpretable, which may not be capable to provide biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using the data from HCP and OASIS. Our results from extensive experiments demonstrate the superiority of the proposed model compared to several state-of-the-art techniques. Additionally, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.