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

Bethlehem Tassew, Muhammad Adeel Ijaz, Geng Chen, Islem Rekik

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
编辑Mario Cannataro, Huiru Zheng, Lin Gao, Jianlin Cheng, Joao Luis de Miranda, Ester Zumpano, Xiaohua Hu, Young-Rae Cho, Taesung Park
出版商Institute of Electrical and Electronics Engineers Inc.
6970-6976
页数7
ISBN(电子版)9798350386226
DOI
出版状态已出版 - 2024
活动2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 - Lisbon, 葡萄牙
期限: 3 12月 20246 12月 2024

出版系列

姓名Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

会议

会议2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
国家/地区葡萄牙
Lisbon
时期3/12/246/12/24

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