Tensor-based Complex-valued Graph Neural Network for Dynamic Coupling Multimodal brain Networks

Abstract

The multi-modal neuroimage study has dramatically facilitated disease diagnosis. Tensor-based methods are commonly used to represent multi-modal data as multi-dimensional arrays and usually implement matrix decomposition. These methods can be seen as a linear algebraic way for the lossy compression of an array. However, involved lossy operations might have a negative impact on performance, and overlook underlying important complementary information between modalities. This study proposes a Tensor-based Complex-valued Graph Neural Network (TC-GNN) to model multimodal neuroimages as complex-valued tensor graphs by investigating underlying complementary associations and cross-modality message aggregation. Experiments on two real-world datasets demonstrate our method’s consistent improvements and superiority over other baseline models in multi-modal brain disease analysis.