… graphs with application to brain connectomes. First, we root our brain graph super-resolution
(BGSR) framework in learning … In this work, we have synthesized LR brain networks using …

… PE [21] with brain network structure to boost the predictive power. Let G = (V, E) be a graph
with the … We begin by learning 3 weights to transform the node features into multiple views. …

… methods involved in machine learning and functional brain network modelling. … develop
new graph learning models; (4) We discuss the relationship between graph learning and several …

… on investigating the multi-view multi-graph embedding problem for brain network clustering
analysis. Specifically, we aim to learn the latent embedding representation of multiple brain …

… performs well across brain networks constructed from different … learn a globally shared
edge mask \(\boldsymbol{M}\in \mathbb {R}^{M \times M}\) that is applied to all brain network …

Neuroanatomy has entered a new era, culminating in the search for the connectome,
otherwise known as the brain’s wiring diagram. While this approach has led to landmark …

… different brain network … graph learning tasks such as graph prediction, classification and
integration with application to network neuroscience where the main data structure is brain …

… between the accuracy and brain network connectivity or learning rate is non-linear. It means
the results will not be significantly affected by extreme connectivity or learning rates and are …

… ) brain networks … brain networks and the underlying pathophysiological mechanisms.
Specifically, noninvasive imaging of structural and functional brain networks and follow-up graph-…

… aggregation; (4) spatial graph pooling based on the functional connectivity to … brain network.
We describe the details of these components and discuss how this ST-graph-conv network …