Results 11 to 20 of about 6,341,676 (193)
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
Graph Ordering Attention Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, in which every node aggregates information from its neighbors using a permutation-invariant aggregation function.
Michail Chatzianastasis +3 more
openaire +4 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
Personalized Pagerank Graph Attention Networks [PDF]
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Published as a conference paper at ICASSP ...
Choi, Julie
core +5 more sources
Evolving graph attention networks for dynamic link prediction. [PDF]
PLoS ONEGraph 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 +6 more
doaj +2 more sources
Fusing multiplex heterogeneous networks using graph attention-aware fusion networks [PDF]
Scientific Reports, 2023 Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type.
Ziynet Nesibe Kesimoglu, Serdar Bozdag
doaj +2 more sources
Hyperbolic Heterogeneous Graph Attention Networks [PDF]
Companion Proceedings of the ACM Web Conference 2024Accepted in ACM THE WEB CONFERENCE 2024 short paper ...
Jongmin Park +3 more
openaire +5 more sources
MGATs: Motif-Based Graph Attention Networks
MathematicsIn 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 +3 more
doaj +2 more sources
Mahalanobis Distance-Based Graph Attention Networks
IEEE AccessIn 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 +2 more
doaj +2 more sources
Hyperbolic Graph Attention Network [PDF]
IEEE Transactions on Big Data, 2021 Graph neural network (GNN) has shown superior performance in dealing with graphs, which has attracted considerable research attention recently. However, most of the existing GNN models are primarily designed for graphs in Euclidean spaces. Recent research has proven that the graph data exhibits non-Euclidean latent anatomy.
Yiding Zhang +4 more
openaire +3 more sources