• Hierarchical graph learning with convolutional network for brain ...🔍
• Triplet Graph Convolutional Network for Multi|scale Analysis of ...🔍
• Graph convolutional network for fMRI analysis based on connectivity ...🔍
• Two|stream Graph Convolutional Networks for 2D Skeleton|based ...🔍
• Graph Convolutional Neural Network Based Emotion Recognition ...🔍
• Spatial–temporal graph convolutional network for Alzheimer ...🔍
• Graph Neural Network and Some of GNN Applications🔍
• Graph convolutional network|based feature selection for high ...🔍

Hierarchical graph learning with convolutional network for brain ...

In computer-aided diagnostic systems, the functional connectome approach has become a common method for detecting neurological disorders.

Triplet Graph Convolutional Network for Multi-scale Analysis of ...

However, existing GCN-based methods usually use one specific template for brain ROI parcellation, which limits the analysis to a single spatial scale. (i.e., a ...

STA-GCN: two-stream graph convolutional network with spatial ...

AbstractSkeleton-based hand gesture recognition is an active research topic in computer graphics and computer vision and has a wide range of applications in ...

Graph convolutional network for fMRI analysis based on connectivity ...

Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis.

Two-stream Graph Convolutional Networks for 2D Skeleton-based ...

A 2D skeleton-based fall detection method relying on the graph convolutional networks in this paper that exceeds baseline method on both the benchmark ...

Graph Convolutional Neural Network Based Emotion Recognition ...

... the binary brain network. Thirdly, the GERBN model that includes six layers is designed to classify and recognize emotional states on the two ...

Spatial–temporal graph convolutional network for Alzheimer ...

For the diagnosis of AD based on EEG, there are two main development areas in recent years. The first area is statistically topological brain ...

Graph Neural Network and Some of GNN Applications - neptune.ai

Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs.

Graph convolutional network-based feature selection for high ...

We present a deep learning-based method—GRAph Convolutional nEtwork feature Selector (GRACES)—to select important features for HDLSS data ...

New Graph-Blind Convolutional Network for Brain Connectome ...

2.2 Graph Convolutional Network without Pre-defined Graph. Structure. Standard GCN, as well as its variants, defines the graph convolution based on a known ...

Two Stream Multi-Attention Graph Convolutional Network for ...

The skeleton-based action recognition has attracted much attention of researchers. The existing methods mostly introduce motion information into models by ...

Two-Stream Adaptive Graph Convolutional Networks for Skeleton ...

A novel two-stream adaptive graph convolutional network (2s-AGCN) for skeleton-based action recognition that increases the flexibility of the model for ...

Graph-generative neural network for EEG-based epileptic seizure ...

This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp ...

Graph neural network - Wikipedia

A graph neural network (GNN) belongs to a class of artificial neural networks for processing data that can be represented as graphs.

Bayesian Functional Connectivity and Graph Convolutional Network ...

This paper proposes a score-based BSL to estimate the dynamic functional connectivity of the brain in sensor space. The Bayesian score has the ...

Revisiting Graph Based Collaborative Filtering: A Linear Residual ...

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution ...

Spatial Downscaling of Streamflow Data with Attention Based Spatio ...

Our study introduces a neural network-based method for estimating historical hourly streamflow in two spatial downscaling scenarios.

Graph Convolutional Networks with Motif-based Attention

Convolutional Two-Stream Network Fusion for Video Action Recognition. In ... XSun JDong YWu CWang BXiang J(2025)HAGCN: A hybrid-order brain network ...

Graph Neural Networks, Part II: Graph Convolutional Networks - Sertis

This series of posts aims to talk about the concept and applications of graph neural networks (GNNs), which is a machine learning model ...

Heterogeneous Graph Convolutional Neural Network via Hodge ...

Abstract. This study proposes a novel heterogeneous graph convolu- tional neural network (HGCNN) to handle complex brain fMRI data at.