… brain networks in ASD vis-à-vis healthy controls. We introduce a new framework for extracting
overlapping dynamic functional brain networks to … dynamic functional brain networks and …

… dynamic graph learning approach to generate an ensemble of subject-specific dynamic graph
embeddings, which allows us to use brain networks … a representation learning process for …

… To address this issue, we propose a signed graph learning model with an interpretable
graph pooling module. Previous studies indicated that brain networks are hierarchically …

… propose a graph transformer geometric learning framework to model the multimodal brain
network constructed by structural MRI (sMRI) and diffusion tensor imaging (DTI) for brain age …

… We propose a general graph self-supervised learning paradigm for brain network analysis,
… learning with masked autoencoding on the brain network analysis by taking into the graph …

… brain network graph topology feature learning strategy to efficiently identify discriminative
graph topologies in emotional EEG brain networks… patterns from EEG brain networks related to …

… of the high dimensional brain networks, and quantifying topological … brain imaging models
and insights into brain mechanisms through the lens of brain networks and graph learning…

… graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a
signed graph … We propose a signed graph encoder to embed the functional brain networks. …

… in brain disorder identification. We propose a novel graph deep learning framework that …
information inherent in the graph structure for classifying brain networks in major depressive …

… original brain network with a large number of noisy connections, we propose graph pooling
… Then, based on the coarsened brain networks, we propose a graph GAN model, named EG-…