… 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 …

… 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 …

… on high-order Variational Graph Autoencoder (VGAE) and graph theory to learn the probability
distribution of the graph used to extract the data model of tasks from brain regions using a …

… 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 …

… 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 …

… 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 …

… the human brain as a brain graph (or brain network) based … of brain graphs commonly used
in brain graph learning, as illustrated in Figure 2 with a toy example. Within each brain graph …

… 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 …

… multi-channel attention graph learning model for embedding EEG signals and classifying
brain networks. • The proposed model can adaptively generate brain functional topology by …

… 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. …