Deep Hypergraph U-Net for Brain Graph Embedding and Classification
This work addresses the need for better diagnostic markers in neurological disorders by improving classification accuracy for brain connectivity analysis, though it is incremental as it builds on existing geometric deep learning methods.
The paper tackled the problem of capturing higher-order relationships in brain network data for classification by proposing Hypergraph U-Net (HUNet), which outperformed state-of-the-art methods with a 4-14% gain in classification accuracy on brain connectomic datasets.
-Background. Network neuroscience examines the brain as a complex system represented by a network (or connectome), providing deeper insights into the brain morphology and function, allowing the identification of atypical brain connectivity alterations, which can be used as diagnostic markers of neurological disorders. -Existing Methods. Graph embedding methods which map data samples (e.g., brain networks) into a low dimensional space have been widely used to explore the relationship between samples for classification or prediction tasks. However, the majority of these works are based on modeling the pair-wise relationships between samples, failing to capture their higher-order relationships. -New Method. In this paper, inspired by the nascent field of geometric deep learning, we propose Hypergraph U-Net (HUNet), a novel data embedding framework leveraging the hypergraph structure to learn low-dimensional embeddings of data samples while capturing their high-order relationships. Specifically, we generalize the U-Net architecture, naturally operating on graphs, to hypergraphs by improving local feature aggregation and preserving the high-order relationships present in the data. -Results. We tested our method on small-scale and large-scale heterogeneous brain connectomic datasets including morphological and functional brain networks of autistic and demented patients, respectively. -Conclusion. Our HUNet outperformed state-of-the-art geometric graph and hypergraph data embedding techniques with a gain of 4-14% in classification accuracy, demonstrating both scalability and generalizability. HUNet code is available at https://github.com/basiralab/HUNet.