Explainable Classification of Brain Networks via Contrast Subgraphs

TL;DR

This paper introduces a novel, interpretable method for classifying brain networks by extracting contrast subgraphs, achieving high accuracy and aligning with neuroscience knowledge.

Contribution

The paper presents a new contrast subgraph extraction approach for brain network classification, combining interpretability with superior accuracy over existing methods.

Findings

Effective identification of contrast subgraphs in brain networks

High classification accuracy matching neuroscience expectations

Method outperforms complex state-of-the-art techniques

Abstract

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.