Scalable and Efficient Multigraph Integration with Sparse-Clustered Deep Graph Normalization

In recent years, Connectional Brain Templates (CBTs) have become essential tools in network neuroscience for the representation of neural connections across populations. These graph-based representations are invaluable for comparing brain connectivity across individuals and identifying deviations associated with neurological and psychiatric disorders. Traditional methods for creating CBTs, such as linear averaging, struggle to capture the non-linear relationships within brain networks, limiting their effectiveness. The Deep Graph Normalizer (DGN), although it has been effective in fusing multi-view brain networks, but encounters significant challenges when the size of the data grows, thus leading to scalability issues and memory bottlenecks. These limitations restrict DGN's applicability to large-scale brain networks, hindering further progress in the field. To address these challenges, we propose the Sparse-Clustered Deep Graph Normalizer Network (SCDGN), an enhanced version of DGN designed to improve scalability and computational efficiency. SCDGN integrates sparsification and hierarchical clustering within the DGN framework, enabling it to learn a cluster assignment matrix over nodes using the output of a GNN model. This approach allows SCDGN to process large graphs more effectively, reducing memory usage by 35% compared to DGN on normal and patient datasets while maintaining computational efficiency. Our experimental results demonstrate that SCDGN can handle large-scale connectomic datasets, offering a robust solution for estimating CBTs in complex brain networks.