Multiplex Graph Networks for Multimodal Brain Network Analysis
This work addresses brain disorder diagnosis by analyzing multimodal brain networks, representing an incremental improvement with a novel method for a known bottleneck in the domain.
The paper tackles multimodal brain network analysis by proposing MGNet, a multiplex graph convolutional network that integrates tensor representation to extract latent structures, achieving state-of-the-art performance in classification tasks on HIV and Bipolar disorder datasets.
In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis. The proposed method integrates tensor representation into the multiplex GCN model to extract the latent structures of a set of multimodal brain networks, which allows an intuitive 'grasping' of the common space for multimodal data. Multimodal representations are then generated with multiplex GCNs to capture specific graph structures. We conduct classification task on two challenging real-world datasets (HIV and Bipolar disorder), and the proposed MGNet demonstrates state-of-the-art performance compared to competitive benchmark methods. Apart from objective evaluations, this study may bear special significance upon network theory to the understanding of human connectome in different modalities. The code is available at https://github.com/ZhaomingKong/MGNets.