Brain Network Classification Based on Graph Contrastive Learning and Graph Transformer
This work addresses data scarcity and supervision issues in functional brain network analysis, which is important for understanding human brain function, though it appears incremental as it builds on existing graph neural network methods.
The paper tackled the problem of limited training data and insufficient supervision in brain network classification by proposing a model that integrates graph contrastive learning with graph transformers, achieving state-of-the-art performance on real-world datasets.
The dynamic characterization of functional brain networks is of great significance for elucidating the mechanisms of human brain function. Although graph neural networks have achieved remarkable progress in functional network analysis, challenges such as data scarcity and insufficient supervision persist. To address the limitations of limited training data and inadequate supervision, this paper proposes a novel model named PHGCL-DDGformer that integrates graph contrastive learning with graph transformers, effectively enhancing the representation learning capability for brain network classification tasks. To overcome the constraints of existing graph contrastive learning methods in brain network feature extraction, an adaptive graph augmentation strategy combining attribute masking and edge perturbation is implemented for data enhancement. Subsequently, a dual-domain graph transformer (DDGformer) module is constructed to integrate local and global information, where graph convolutional networks aggregate neighborhood features to capture local patterns while attention mechanisms extract global dependencies. Finally, a graph contrastive learning framework is established to maximize the consistency between positive and negative pairs, thereby obtaining high-quality graph representations. Experimental results on real-world datasets demonstrate that the PHGCL-DDGformer model outperforms existing state-of-the-art approaches in brain network classification tasks.