… We focus on the problem of brain network representation learning based on DRL-introduced
GNNs, which is used for both classification and clustering. Specifically, we focus on the …

… classification capability of graph convolutional networks (GCNs). … graph learning framework
for brain network classification, involving multi-graph clustering, graph generation and graph …

… While applied to brain networks, our proposed method is flexible and general enough to
be applied to any problem involving comparisons between graphs, eg shape analysis. …

… to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN…
While applied to brain networks, our proposed method is flexible and general enough to be …

… learning deep representations from fMRI brain connectivity networks, where each brain network
represents the brain … of jointly learning the graph embedding from both the aspects of the …

… coherent brain “areas” that form the building blocks of structural or functional brain networks.
… Citation14 A recent multimodal study employed machine learning to extract coherent brain …

… Several pioneering deep models have been devised to predict brain diseases by learning
graph structures of brain networks. For instance, Li et al. [1] propose BrainGNN to analyze …

… 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 …

… brain. To solve this problem, we propose a temporal graph representation learning framework
for brain networks (… The framework involves a temporal graph pooling for eliminating the …

… graph learning framework for brain network regression analysis. We demonstrated that
our new framework has better prediction performances than state-of-the-arts graph learning …