# Structure-Preserving Graph Kernel for Brain Network Classification

arXiv: https://arxiv.org/abs/2111.10803 · HTML: https://mlsanity.com/paper/2111.10803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 21 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease](https://mlsanity.com/paper/1805.08801/) (2018)
- [DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages](https://mlsanity.com/paper/1407.8289/) (2014)

## Abstract (arXiv)

This paper presents a novel graph-based kernel learning approach for connectome analysis. Specifically, we demonstrate how to leverage the naturally available structure within the graph representation to encode prior knowledge in the kernel. We first proposed a matrix factorization to directly extract structural features from natural symmetric graph representations of connectome data. We then used them to derive a structure-persevering graph kernel to be fed into the support vector machine. The proposed approach has the advantage of being clinically interpretable. Quantitative evaluations on challenging HIV disease classification (DTI- and fMRI-derived connectome data) and emotion recognition (EEG-derived connectome data) tasks demonstrate the superior performance of our proposed methods against the state-of-the-art. Results showed that relevant EEG-connectome information is primarily encoded in the alpha band during the emotion regulation task.