s the robustness of solutions. For this purpose, we organized an in-class challenge via [@Kaggle][^1], where participants aim to classify ASD/NC subjects using solely CMNs derived from maximum principal curvature of the cortical surface. Teams were ranked based on three classification performance metrics: accuracy, sensitivity and specificity, evaluated on a a hidden test dataset. The final ranks of teams were determined by summing ranks on each individual metric. This challenge fills the gap emerging from the lack of studies on morphological brain networks for ASD diagnosis, by enabling an assessment of a wide range of methods through standardized performance metrics.

In this manuscript, we present the results for the competition and describe the computational approaches of the top 20 ranked teams. We provide the comparison and comprehensive characterization of different ASD/NC ML classification methods on cortical morphological networks and interpret the performance of each method. Promoting open ML in network neuroscience, we have shared the Python network classification codes by the top 20 participating teams on BASIRA Lab GitHub: <https://github.com/basiralab/BrainNet-ML-ToolBox>, which were polished by the second-first author.

Methods
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Competition Organization
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The diagnosis of neurological disorders, such as ASD, can be formulated as a classification task in machine learning. In this case, the learning model being chosen for this task is fed by a training dataset in which each sample is represented with brain connectome map (e.g., CMN) of a subject and a binary classification label of the subject as with or without disorder. In the case of highly-qualified generalizability of the ML model to unseen data in the testing phase, such a learning model can be a pre-eminent method for the diagnosis of a particular brain disorder. To evaluate the generalizability of a wide spectrum of ML methods in diagnosing ASD patients using CMNs, a competition was set up in Kaggle platform. T