  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.
