Results 41 to 50 of about 5,698,498 (295)

Understanding Spectral Graph Neural Network [PDF]

, 2023
Graph neural networks have developed by leaps and bounds in recent years due to the restriction of traditional convolutional filters on non-Euclidean structured data.
Chen, Xinye
core   +1 more source

Dual graph convolutional neural network for predicting chemical networks

BMC Bioinformatics, 2020
Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery.
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima   +6 more
doaj   +1 more source

Hyperbolic Graph Convolutional Neural Networks

Advances in neural information processing systems, 2019
Published at Conference NeurIPS 2019.
Ines Chami, Rex Ying, Christopher Ré, Jure Leskovec   +3 more
openaire   +5 more sources

Kernel Graph Convolutional Neural Networks [PDF]

, 2018
Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. This two-stage approach decouples data representation from learning, which is suboptimal.
Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis   +4 more
openaire   +3 more sources

Graph Capsule Convolutional Neural Networks

CoRR, 2018
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision.
Saurabh Verma, Zhi-Li Zhang
openaire   +2 more sources

Human Action Recognition Algorithm Based on Adaptive Shifted Graph Convolutional NeuralNetwork with 3D Skeleton Similarity [PDF]

Jisuanji kexue
Graph convolutional neural network(GCN) has achieved good results in the field of human action recognition based on 3D skeleton.However,in most of the existing GCN methods,the construction of the behavior diagram is based on the manual setting of the ...
YAN Wenjie, YIN Yiying
doaj   +1 more source

Graph Based Convolutional Neural Network

CoRR, 2016
11 pages, accepted into BMVC ...
Michael Edwards, Xianghua Xie
openaire   +3 more sources

Energy-efficient Graph Convolutional Neural Network Accelerator with optimized dataflow

, 2022
We propose a Graph Convolutional Neural Network Accelerator with optimal dataflow to improve energy efficiency and ...
Liu, Siqin
core   +2 more sources

Tensor graph convolutional neural network

CoRR, 2018
In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and the other is cross-attribute graphs.
Tong Zhang 0021, Wenming Zheng, Zhen Cui 0001, Yang Li 0019   +3 more
openaire   +3 more sources

Graph convolutional neural networks via scattering

Applied and Computational Harmonic Analysis, 2020
26 pages, 9 figures, 4 ...
Dongmian Zou, Gilad Lerman
openaire   +4 more sources