# Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis

arXiv: https://arxiv.org/abs/1811.02662 · HTML: https://mlsanity.com/paper/1811.02662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)

## Abstract (arXiv)

Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space. While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such as fMRI and DTI images. We propose an end-to-end similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis. The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks using two graph convolutional networks as the twin networks. Our proposed framework performs higher-order convolutions by incorporating higher-order proximity in graph convolutional networks to characterize and learn the community structure in brain connectivity networks. To the best of our knowledge, this is the first community-preserving similarity learning framework for multi-subject brain network analysis. Experimental results on four real fMRI datasets demonstrate the potential use cases of the proposed framework for multi-subject brain analysis in health and neuropsychiatric disorders. Our proposed approach achieves an average AUC gain of 75% compared to PCA, an average AUC gain of 65.5% compared to Spectral Embedding, and an average AUC gain of 24.3% compared to S-GCN across the four datasets, indicating promising application in clinical investigation and brain disease diagnosis.