
FastGCN
 Referenced in 9 articles
[sw38089]
 FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. The graph convolutional networks ... graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions...

DropEdge
 Referenced in 3 articles
[sw37753]
 DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. Overfitting and oversmoothing ... main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over ... input features with the increase in network depth. This paper proposes DropEdge, a novel ... certain number of edges from the input graph at each training epoch, acting like...

LightGCN
 Referenced in 2 articles
[sw37571]
 LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. Graph Convolution Network (GCN) has become ... which is originally designed for graph classification tasks and equipped with many neural network operations...

CayleyNets
 Referenced in 5 articles
[sw38090]
 CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters. The rise of graphstructured ... social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success ... spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model...

Pixel2Mesh
 Referenced in 5 articles
[sw31205]
 represents 3D mesh in a graphbased convolutional neural network and produces correct geometry...

DeepGCNs
 Referenced in 2 articles
[sw38086]
 GCNs Go as Deep as CNNs? Convolutional Neural Networks (CNNs) achieve impressive performance ... Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent ... from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures. Extensive...

MSRGCN
 Referenced in 1 article
[sw42692]
 MultiScale Residual Graph Convolution Networks for Human Motion Prediction. Human motion prediction ... aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective ... propose a novel MultiScale Residual Graph Convolution Network (MSRGCN) for human pose prediction...

GaitGraph
 Referenced in 1 article
[sw42734]
 GaitGraph: Graph Convolutional Network for SkeletonBased Gait Recognition. Gait recognition is a promising video ... GaitGraph that combines skeleton poses with Graph Convolutional Network (GCN) to obtain a modern model...

CGCNet
 Referenced in 1 article
[sw39633]
 Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images. Colorectal cancer (CRC) grading ... present a novel cellgraph convolutional neural network (CGCNet) that converts each large histology ... introduce Adaptive GraphSage, which is a graph convolution technique that combines multilevel features...

Pose2Mesh
 Referenced in 1 article
[sw42752]
 Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human ... propose Pose2Mesh, a novel graph convolutional neural network (GraphCNN)based system that estimates...

RGCNN
 Referenced in 2 articles
[sw36662]
 images before feeding them into neural networks, which leads to voluminous data and quantization artifacts ... instead propose a regularized graph convolutional neural network (RGCNN) that directly consumes point clouds. Leveraging ... point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial...

JGRP2O
 Referenced in 1 article
[sw42731]
 Joint Graph Reasoning based PixeltoOffset Prediction Network for 3D Hand Pose Estimation from ... plain feature representation learning with local convolutions. In this paper, a novel pixelwise prediction ... training. Specifically, we first propose a graph convolutional network (GCN) based joint graph reasoning module ... implemented with an efficient 2D fully convolutional network (FCN) backbone and has only about...

GraphSAINT
 Referenced in 1 article
[sw37758]
 Graph Sampling Based Inductive Learning Method. Graph Convolutional Networks (GCNs) are powerful models for learning...

paper2repo
 Referenced in 1 article
[sw32544]
 paper2repo integrates text encoding and constrained graph convolutional networks (GCN) to automatically learn...

LightTrack
 Referenced in 1 article
[sw39261]
 tracking. We also propose a Siamese Graph Convolution Network (SGCN) for human pose matching...

SportsCap
 Referenced in 1 article
[sw40782]
 introduce a multistream spatialtemporal Graph Convolutional Network(STGCN) to predict the fine...

PiNN
 Referenced in 2 articles
[sw30601]
 Networks of Molecules and Materials. Atomic neural networks (ANNs) constitute a class of machine learning ... PiNN, we implemented an interpretable graph convolutional neural network variant, PiNet, as well...

SyncSpecCnn
 Referenced in 5 articles
[sw26163]
 with images that are 2D grids, shape graphs are irregular and nonisomorphic data structures ... vertex functions on them by convolutional neural networks, we resort to spectral CNN method that ... kernels in the spectral domain spanned by graph laplacian eigenbases. Under this setting, our network ... different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels...

Chainer Chemistry
 Referenced in 1 article
[sw39646]
 models (especially GCNN  Graph Convolutional Neural Network) for chemical property prediction...

DeepLGP
 Referenced in 0 articles
[sw37448]
 this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing...