Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed ...

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
GitHub

Spatial Temporal Graph Convolutional Networks (ST-GCN)

Our demo for skeleton based action recognition: ST-GCN is able to exploit local pattern and correlation from human skeletons. Below figures show the neural response magnitude of each node in the last ...
C&EN

GGCRB: A Graph Neural Network Approach for Predicting CircRNA–RBP Interactions Using ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The interaction between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) plays ...
C&EN

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case ...

Department of Chemistry and Biochemistry, University of Wisconsin─Eau Claire, Eau Claire, Wisconsin 54702, United States ...
IEEE

Cross-Domain Explainable Recommendation Using Graph Convolutional Networks and a Topic Model

Abstract: Graph Convolutional Network (GCN)-based recommendation systems (RSs) have recently gained popularity for their ability to improve recommendation accuracy by utilizing neighborhood ...
Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on ...

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...
Oak Ridge National Lab

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...
IEEE

A Survey on Skeleton-Based Activity Recognition using Graph Convolutional Networks (GCN)

Abstract: Skeleton-Based Activity recognition is an active research topic in Computer Vision. In recent years, deep learning methods have been used in this area, including Recurrent Neural Network ...