… concept of graph convolutions and describe how the filtering operation can be performed
in the graph spectral domain in 2.3. Finally, we describe the proposed network architecture in …

… -scale spatial and topology information of brain connectivity networks, we employ T different
… in this work, as a special form of Laplacian smoothing, convolution operations on each node …

… convolution operations in the neural network to create a feature representation from the
timeseries belonging to each node. More specifically, we employed 4 layers of 1D Convolution, …

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

… work concerning GNN analysis of brain graphs can be built. … brain connectivity analysis
to other fields where graph … to dense graphs as convolutions performed in the graph domain …

… Graph convolution uses the eigenvectors of the Laplacian matrix L in Eq.1 as the bases of
the graph … human cognitive ability and brain connectivity networks by applying the proposed …

… In order to capture the complex relationship buried in both direct and indirect brain connections…
introduce the notations of a graph and the graph convolution operation used in this work. …

… have been employed to address both graph-centric and node… case, convolutions correspond
to multiplications in the graph … graph convolutions and attention mechanisms for graph- and …

… on brain connectivity networks (ie, functional networks) extracted from the input fMRI images,
where the observed brain … networks can be analyzed/modeled using graphtheoretic and …

… the effect of normal ageing on brain network characteristics before we can accurately diagnose
… Based on the concept of a spectral graph convolution, we introduce the notion of a graph …