Results 51 to 60 of about 145,055 (276)

Graph Classification with 2D Convolutional Neural Networks

, 2019
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial.
Meladianos, Polykarpos, Nikolentzos, Giannis, Tixier, Antoine Jean-Pierre, Vazirgiannis, Michalis   +3 more
core   +2 more sources

Graph convolutional networks for graphs containing missing features

Future Generation Computer Systems, 2021
Graph Convolutional Network (GCN) has experienced great success in graph analysis tasks. It works by smoothing the node features across the graph. The current GCN models overwhelmingly assume that the node feature information is complete. However, real-world graph data are often incomplete and containing missing features.
Hibiki Taguchi, Xin Liu 0020, Tsuyoshi Murata   +2 more
openaire   +2 more sources

Texoskeletons: Developing the Fundamental Technologies for Creating Intelligent Soft Robotic Clothing With Integrated 1D Sensors and Actuators

Advanced Functional Materials, EarlyView.
ABSTRACT Traditional wearable exoskeletons rely on rigid structures, which limit comfort, flexibility, and everyday usability. This work introduces the fundamental technologies to create the first soft, lightweight, intelligent textile‐based exoskeletons (Texoskeletons) built using 1D sensors and actuators.
Amy Lukomiak, Rameesh Bulathsinghala, Jack Varga, Shuxin Meng, Kalana Marasinghe, Imaad Refai, Biyon Fernando, Amitkumar Patel, Chamika M. Halloluwa‐Arachchige, Carlos Oliveira, Arash M. Shahidi, Zahra Rahemtulla, Alexander Turner, Ziyun Ding, Bernard X. W. Liew, Andy Kerr, Ezio Preatoni, Theo Hughes‐Riley, Ishara Dharmasena, Pasindu Lugoda   +19 more
wiley   +1 more source

Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs

IEEE Access, 2020
Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li, Xiaowei Qin, Xiaodong Xu, Dujia Yang, Guo Wei   +4 more
doaj   +1 more source

Single-cell classification using graph convolutional networks

BMC Bioinformatics, 2021
Background Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research.
Tianyu Wang, Jun Bai, Sheida Nabavi
doaj   +1 more source

ADSTGCN: A Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Network for Multi-Step Traffic Forecasting

Sensors, 2023
Multi-step traffic forecasting has always been extremely challenging due to constantly changing traffic conditions. Advanced Graph Convolutional Networks (GCNs) are widely used to extract spatial information from traffic networks.
Zhengyan Cui, Junjun Zhang, Giseop Noh, Hyun Jun Park   +3 more
doaj   +1 more source

Relational graph convolutional networks: a closer look

PeerJ Computer Science, 2022
In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction
Thiviyan Thanapalasingam, Lucas van Berkel, Peter Bloem, Paul Groth   +3 more
openaire   +7 more sources

Packed for Ossification: High‐Density Bioprinting of hPDC Spheroids in HAMA Toward Endochondral Ossification

Advanced Healthcare Materials, EarlyView.
Human periosteum‐derived cell spheroids bioprinted at high density within a hyaluronic acid matrix promote fusion and hypertrophic cartilage formation in vitro. Early encapsulation enhances spheroid interaction and matrix maturation, generating scalable cartilage templates intended for endochondral bone regeneration.
Ane Albillos Sanchez, Maria Paula Marks, Paula Casademunt, Adrián Seijas‐Gamardo, Ioannis Papantoniou, Lorenzo Moroni, Carlos Mota   +6 more
wiley   +1 more source

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Scientific Reports
In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power.
Bin Wang, Bodong Cai, Jinfang Sheng, Wenzhe Jiao   +3 more
doaj   +1 more source

Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets

Machine Learning with Applications
In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes.
Parisa Foroutan, Salim Lahmiri
doaj   +1 more source