TY - GEN
T1 - Scalable and Efficient Multigraph Integration with Sparse-Clustered Deep Graph Normalization
AU - Tassew, Bethlehem
AU - Ijaz, Muhammad Adeel
AU - Chen, Geng
AU - Rekik, Islem
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Brain connectivity mapping
KW - Connectional brain templates
KW - Deep graph normalizer
KW - Scalability
UR - http://www.scopus.com/inward/record.url?scp=85217276396&partnerID=8YFLogxK
U2 - 10.1109/BIBM62325.2024.10822066
DO - 10.1109/BIBM62325.2024.10822066
M3 - 会议稿件
AN - SCOPUS:85217276396
T3 - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
SP - 6970
EP - 6976
BT - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
A2 - Cannataro, Mario
A2 - Zheng, Huiru
A2 - Gao, Lin
A2 - Cheng, Jianlin
A2 - de Miranda, Joao Luis
A2 - Zumpano, Ester
A2 - Hu, Xiaohua
A2 - Cho, Young-Rae
A2 - Park, Taesung
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
Y2 - 3 December 2024 through 6 December 2024
ER -