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In this paper, we tackle a problem of predicting phenotypes from structuralconnectomes. We propose that normalized Laplacian spectra can capturestructural properties of brain networks, and hence graph spectral distributionsare useful for a task of connectome-based classification. We introduce a kernelthat is based on earth mover's distance (EMD) between spectral distributions ofbrain networks. We access performance of an SVM classifier with the proposedkernel for a task of classification of autism spectrum disorder versus typicaldevelopment based on a publicly available dataset. Classification quality (areaunder the ROC-curve) obtained with the EMD-based kernel on spectraldistributions is 0.71, which is higher than that based on simpler graphembedding methods.

Kernel classification of connectomes based on earth mover's distance between graph spectra