… INTRODUCTION In recent years, the conceptualisation of the brain as a “connectome”, in …
derived from graph theory analyses, has become increasingly popular. Brain networks derived …

… and community structure of brain networks, we investigate the resulting brain network embedding
by the higher-order GCN. For each brain network, we cluster the brain regions (nodes) …

… 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 …

… brain networks in the graph convolutions by considering the intra-community and inter-community
properties in the learning … focus on learning a similarity metric on fMRI brain networks, …

… 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 …

… to automatically learn the relationship between brain connectivity network and human … the
graph-structured brain network data and cannot utilize topological structures of brain network. …

… utilizing graph-based methods to analyze connectivity patterns in the human brain network
using … the efficiency of combining graph theory and machine learning for early detection of AD …

… graph learning methods to study the brain network … the graph learning problem for brain
network classification, we propose a graph convolution aggregator based on extreme learning …

… To take into account the intrinsic locality of structural brain networks, we propose a novel
graph convolutional network (GCN) to learn each region’s representation by propagating node-…

… The identified key brain subnetworks show significant between-group difference and … -based
graph representation learning on brain networks to model human brain activity and function. …