Results 11 to 20 of about 1,049,471 (309)
Graph Attention Networks: A Comprehensive Review of Methods and Applications
Future InternetReal-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring ...
Aristidis G. Vrahatis +2 more
doaj +4 more sources
International Conference on Learning Representations, 2017 We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their ...
Petar Velickovic +5 more
semanticscholar +3 more sources
FairGAT: Fairness-Aware Graph Attention Networks [PDF]
ACM Transactions on Knowledge Discovery from Data, 2023 Graphs can facilitate modeling various complex systems such as gene networks and power grids as well as analyzing the underlying relations within them.
Öykü Deniz Köse, Yanning Shen
semanticscholar +3 more sources
Advanced intrusion detection in internet of things using graph attention networks. [PDF]
Sci RepInternet of Things (IoT) denotes a system of interconnected devices equipped with processors, sensors, and actuators that capture and exchange meaningful data with other smart systems.
Ahanger AS +3 more
europepmc +2 more sources
Crystal graph attention networks for the prediction of stable materials. [PDF]
Sci Adv, 2021 Crystal graph attention networks speed up the prediction of new thermodynamically stable materials in high-throughput searches.
Schmidt J +4 more
europepmc +2 more sources
Graph Oriented Attention Networks
IEEE AccessGraph 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 ...
Ouardi Amine, Mohammed Mestari
doaj +2 more sources
Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks [PDF]
Frontiers in Genetics, 2022 With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity.
Xiang Feng +4 more
doaj +2 more sources
Sparse Graph Attention Networks [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2019 Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many practical predictive tasks, such as node classification, link prediction and ...
Yang Ye, Shihao Ji
semanticscholar +4 more sources
Chromatin interaction-aware gene regulatory modeling with graph attention networks. [PDF]
Genome Res, 2022 Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting non-coding genetic variation.
Karbalayghareh A, Sahin M, Leslie CS.
europepmc +2 more sources
MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction. [PDF]
Int J Mol SciAccurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer.
Tanvir RB +4 more
europepmc +2 more sources