Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks

Brain graphs, which model the structural and functional relationships between brain regions, are crucial in neuroscientific and clinical applications that can be formulated as graph classification tasks. However, dense brain graphs pose computational challenges such as large time and memory consumption and poor model interpretability. In this paper, we investigate effective designs in Graph Neural Networks (GNNs) to sparsify brain graphs by eliminating noisy edges. Many prior works select noisy edges based on explainability or task-irrelevant properties, but this does not guarantee performance improvement when using the sparsified graphs. Additionally, the selection of noisy edges is often tailored to each individual graph, making it challenging to sparsify multiple graphs collectively using the same approach.