Before revision:  In this article, we instead develop a nonlinear latent factor model for summarizing the brain graph in both unsupervised and supervised settings. The proposed approach builds on methods for hierarchical modeling of replicated graph data, as well as variational auto-encoders that use neural networks for dimensionality reduction.
After revision:  In this article, we instead develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits.