Results 31 to 40 of about 106,924 (261)

MBHAN: Motif-Based Heterogeneous Graph Attention Network

, 2022
Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Minli Tang, Qian Hu, Weiping Lin, Jiatao Jiang   +3 more
core   +1 more source

Relational Graph Attention Networks

CoRR, 2019
We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems. A thorough evaluation of these models is performed, and comparisons are made against established benchmarks.
Dan Busbridge, Dane Sherburn, Pietro Cavallo, Nils Y. Hammerla   +3 more
openaire   +2 more sources

Semi-supervised Multi-graph-attention for Breast Cancer Detection

, 2022
Mammography is the most widely used breast screening method for early detection and routine monitoring of breast cancer. Most mammogram classification or segmentation techniques are limited to simple binary classifications of the abnormalities in ...
Mohamed Ibrahim (10114204)
core   +1 more source

Learnable Graph Convolutional Attention Networks

CoRR, 2022
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features. Recent works have shown the strengths and weaknesses of the resulting GNN architectures, respectively, GCNs and GATs.
Adrián Javaloy, Pablo Sánchez-Martín, Amit Levi 0001, Isabel Valera   +3 more
openaire   +3 more sources

Graph Oriented Attention Networks

IEEE Access
Graph Attention Networks (GAT) is a type of neural network architecture designed to effectively model and process data represented as graphs. GATs leverage the concept of attention mechanisms to learn the importance of different nodes in a graph when performing tasks such as node classification or link prediction.
Ouardi Amine, Mohammed Mestari
openaire   +2 more sources

Community Detection Fusing Graph Attention Network

, 2022
It has become a tendency to use a combination of autoencoders and graph neural networks for attribute graph clustering to solve the community detection problem.
Qianqian Bai, Ruiqiang Guo, Juan Zou, Wei Wang, Xiaomeng Chang   +4 more
core   +1 more source

A Bibliometric Analysis of Publications in Uremic Toxins From 1991 to 2024

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Uremic toxins are a growing area of research in nephrology, with significant implications in the progression and treatment of chronic kidney disease (CKD) and the management of end‐stage kidney disease (ESKD). This bibliometric analysis aims to evaluate the global research trends, key contributors, and the impact of publications in ...
Yuh‐Shan Ho, Karim M. Soliman, Wisit Cheungpasitporn, Wannasit Wathanavasin, Ahmed Daoud, Ahmed I. Kamal, Balázs Szamosfalvi, Tibor Fülöp   +7 more
wiley   +1 more source

Deep Graph Attention Networks

2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW)
Graphs are useful for representing various realworld objects. However, graph neural networks (GNNs) tend to suffer from over-smoothing, where the representations of nodes of different classes become similar as the number of layers increases, leading to performance degradation.
Jun Kato 0008, Airi Mita, Keita Gobara, Akihiro Inokuchi   +3 more
openaire   +2 more sources

An unexpected alternative viologen electron mediator site in tungsten‐containing formate dehydrogenase

FEBS Letters, EarlyView.
An unexpected alternative interaction site for ethyl viologen was identified in formate dehydrogenase 1 from Methylorubrum extorquens. Combined mutagenesis, kinetic analysis, and docking revealed that aromatic residues near an iron–sulfur cluster enable flavin mononucleotide‐independent electron transfer, offering a framework for engineering improved ...
Eleni G. Poloniataki, Yong Hwan Kim
wiley   +1 more source

Somatic mutational landscape in von Hippel–Lindau familial hemangioblastoma

Molecular Oncology, EarlyView.
The causes of central nervous system (CNS) hemangioblastoma in Von Hippel–Lindau (vHL) disease are unclear. We used Whole Exome Sequencing (WES) on familial hemangioblastoma to investigate events that underlie tumor development. Our findings suggest that VHL loss creates a permissive environment for tumor formation, while additional alterations ...
Maja Dembic, Anne Nørremølle, Lilian Bomme Ousager, Lars van Brakel Andersen, Marie Louise Mølgaard Binderup, Mads Thomassen   +5 more
wiley   +1 more source