Abstract: Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have ...

We propose a connectivity-based graph convolution network (cGCN ... "Application of convolutional recurrent neural network for individual recognition based on resting state fmri data." Frontiers in ...

To address these issues, we propose a new GNN algorithm, LEGNN (Local and Global Enhanced Graph Neural Network), which introduces several key improvements over traditional GNN models such as GraphSAGE ...

The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the ...

We propose a spherical kernel for efficient graph convolution of 3D point clouds ... The proposed kernel is applied to graph neural networks without edge-dependent filter generation, making it ...

Graph convolutional network (GCN) (Kipf et al., 2017) extends the convolutional neural network to solve non-Euclidean space problems. It uses structural information on the constructed network by ...

GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...

Deep neural networks will allow signal ... the speed of analysis using deep neural networks. The first step involves transforming the time series recordings into two-dimensional histograms. These ...

MIAMI--(BUSINESS WIRE)--Provider Network Solutions (PNS), a leading Managed Service Organization (MSO), has entered into an exciting new partnership with Singular Health Group, an Australian ...