Abstract
Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Network analysis provides a new way for exploring the association between brain functional deficits and the underlying structural disruption related to brain disorders. In this work we propose a Siamese GCN framework (called S-GCN) to learn useful representations for graph classification in an end-to-end fashion. To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Extensive experiments on ADNI dataset have demonstrated competitive performance of the S-GCN model.
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