[KDD 2020] Explainable classification of brain networks via contrast subgraphs
Aug 13, 202042 views
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.