Results 21 to 30 of about 36,148 (258)

Convolutional Graph Neural Networks

2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro   +3 more
openaire   +3 more sources

Adaptive filters in Graph Convolutional Neural Networks

Pattern Recognition, 2023
Over the last few years, we have witnessed the availability of an increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relationships, and Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data.
Andrea Apicella, Francesco Isgrò, Andrea Pollastro, Roberto Prevete   +3 more
openaire   +3 more sources

Transformer-Based Graph Convolutional Network for Sentiment Analysis

Applied Sciences, 2022
Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years.
Barakat AlBadani, Ronghua Shi, Jian Dong, Raeed Al-Sabri, Oloulade Babatounde Moctard   +4 more
doaj   +1 more source

Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network

Future Internet, 2021
Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders.
Ibsa K. Jalata, Thanh-Dat Truong, Jessica L. Allen, Han-Seok Seo, Khoa Luu   +4 more
doaj   +1 more source

Survey of Graph Neural Network in Recommendation System [PDF]

Jisuanji kexue yu tansuo, 2022
Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well.
WU Jing, XIE Hui, JIANG Huowen
doaj   +1 more source

Anomaly detection with convolutional Graph Neural Networks [PDF]

Journal of High Energy Physics, 2021
Abstract We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based
Atkinson, Oliver, Bhardwaj, Akanksha, Englert, Christoph, Ngairangbam, Vishal S., Spannowsky, Michael   +4 more
openaire   +6 more sources

Two-way Feature Augmentation Graph Convolution Networks Algorithm [PDF]

Jisuanji kexue
Graph convolutional neural network algorithms play a crucial role in the processing of graph structured data.The mainstream mode of existing graph convolutional networks is based on weighted summation of node features using Laplacian matrices,with a ...
LI Mengxi, GAO Xindan, LI Xue
doaj   +1 more source

Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method

Applied Sciences, 2023
In order to solve the current problems in domain long text classification tasks, namely, the long length of a document, which makes it difficult for the model to capture key information, and the lack of expert domain knowledge, which leads to ...
Yifei Wang, Yongwei Wang, Hao Hu, Shengnan Zhou, Qinwu Wang   +4 more
doaj   +1 more source

A Convolutional Neural Network into graph space

CoRR, 2020
arXiv admin note: text overlap with arXiv:1611.08402 by other ...
Maxime Martineau, Romain Raveaux, Donatello Conte, Gilles Venturini   +3 more
openaire   +2 more sources

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   +4 more sources