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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 ...
Ouardi Amine, Mohammed Mestari
doaj   +4 more sources

Graph Attention Networks: A Comprehensive Review of Methods and Applications

Future Internet
Real-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, Konstantinos Lazaros, Sotiris Kotsiantis   +2 more
doaj   +4 more sources

Graph Ordering Attention Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance.
Lutzeyer, Johannes F., Lutzeyer, Johannes, Dasoulas, George, Vazirgiannis, Michalis, Chatzianastasis, Michail   +4 more
core   +3 more sources

Personalized PageRank Graph Attention Networks

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
There has been a rising interest in graph neural networks (GNNs) for representation learning over the past few years. GNNs provide a general and efficient framework to learn from graph-structured data.
Choi, Julie
core   +2 more sources

Knowledge Graph Embedding via Graph Attenuated Attention Networks

IEEE Access, 2020
Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural
Rui Wang, Bicheng Li, Shengwei Hu, Wenqian Du, Min Zhang   +4 more
doaj   +3 more sources

Evolving graph attention networks for dynamic link prediction. [PDF]

PLoS ONE
Graph neural networks (GNNs), which learn node representations via aggregating their neighbors, have shown superior performance and become the de facto efficient toolkit for analyzing and learning from data with structured properties.
Yucai Jiang, Rongying Shan, Gang Fu, Zhuolin Li, Jingjing Sun, Zhongyun Bao, Feifei Wei   +6 more
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, Fang Fang, Haixia Long, Rao Zeng, Yuhua Yao   +4 more
doaj   +2 more sources

MGATs: Motif-Based Graph Attention Networks

Mathematics
In recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks.
Jinfang Sheng, Yufeng Zhang, Bin Wang, Yaoxing Chang   +3 more
doaj   +2 more sources

MBHAN: Motif-Based Heterogeneous Graph Attention Network

Applied Sciences, 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
Qian Hu, Weiping Lin, Minli Tang, Jiatao Jiang   +3 more
doaj   +2 more sources

Mahalanobis Distance-Based Graph Attention Networks

IEEE Access
In this paper, a Mahalanobis Distance-based Graph Attention Network for graph classification, is proposed. In contrast to traditional Graph Attention Networks, the proposed approach learns the covariances between node features so as to determine the ...
Konstantina Mardani, Nicholas Vretos, Petros Daras   +2 more
doaj   +2 more sources