  Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and
functional magnetic resonance imaging (fMRI), enable us to model the human
brain as a brain network or connectome. Capturing brain networks' structural
information and hierarchical patterns is essential for understanding brain
functions and disease states. Recently, the promising network representation
learning capability of graph neural networks (GNNs) has prompted many GNN-based
methods for brain network analysis to be proposed. Specifically, these methods
apply feature aggregation and global pooling to convert brain network instances
into meaningful low-dimensional representations used for downstream brain
network analysis tasks. However, existing GNN-based methods often neglect that
brain networks of different subjects may require various aggregation iterations
and use GNN with a fixed number of layers to learn all brain networks.
Therefore, how to fully release the potential of GNNs to promote brain network
analysis is still non-trivial. To solve this problem, we propose a novel brain
network representation framework, namely BN-GNN, which searches for the optimal
GNN architecture for each brain network. Concretely, BN-GNN employs deep
reinforcement learning (DRL) to train a meta-policy to automatically determine
the optimal number of feature aggregations (reflected in the number of GNN
layers) required for a given brain network. Extensive experiments on eight
real-world brain network datasets demonstrate that our proposed BN-GNN improves
the performance of traditional GNNs on different brain network analysis tasks.
