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In this paper, we are going to apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset. Since ASD is characterized by social deficits and repetitive behavioral symptoms, it is mainly identified by brain functional connectivity patterns. Attempts to unveil the neural patterns that emerged from ASD are the essence of ASD classification. We claim that considering the connectivity patterns of the brain can bedoi:10.1101/2022.06.23.497324 fatcat:n6ykpiwdgjeq3cvnmov25ehc3m