Results 31 to 40 of about 142,397 (310)

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

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

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

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

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

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

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

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   +4 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