  Using predictive models to identify patterns that can act as biomarkers for
different neuropathoglogical conditions is becoming highly prevalent. In this
paper, we consider the problem of Autism Spectrum Disorder (ASD) classification
where previous work has shown that it can be beneficial to incorporate a wide
variety of meta features, such as socio-cultural traits, into predictive
modeling. A graph-based approach naturally suits these scenarios, where a
contextual graph captures traits that characterize a population, while the
specific brain activity patterns are utilized as a multivariate signal at the
nodes. Graph neural networks have shown improvements in inferencing with
graph-structured data. Though the underlying graph strongly dictates the
overall performance, there exists no systematic way of choosing an appropriate
graph in practice, thus making predictive models non-robust. To address this,
we propose a bootstrapped version of graph convolutional neural networks
(G-CNNs) that utilizes an ensemble of weakly trained G-CNNs, and reduce the
sensitivity of models on the choice of graph construction. We demonstrate its
effectiveness on the challenging Autism Brain Imaging Data Exchange (ABIDE)
dataset and show that our approach improves upon recently proposed graph-based
neural networks. We also show that our method remains more robust to noisy
graphs.
