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Tremendous recent literature show that associations between different brainregions, i.e., brain connectivity, provide early symptoms of neurologicaldisorders. Despite significant efforts made for graph neural network (GNN)techniques, their focus on graph nodes makes the state-of-the-art GNN methodsnot suitable for classifying brain connectivity as graphs where the objectiveis to characterize disease-relevant network dysfunction patterns on graphlinks. To address this issue, we propose Multi-resolution Edge Network (MENET)to detect disease-specific connectomic benchmarks with high discriminationpower across diagnostic categories. The core of MENET is a novel graphedge-wise transform that we propose, which allows us to capturemulti-resolution ``connectomic'' features. Using a rich set of the connectomicfeatures, we devise a graph learning framework to jointly select discriminativeedges and assign diagnostic labels for graphs. Experiments on two real datasetsshow that MENET accurately predicts diagnostic labels and identify brainconnectivities highly associated with neurological disorders such asAlzheimer's Disease and Attention-Deficit/Hyperactivity Disorder.

Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders