Results 41 to 50 of about 6,341,676 (193)
Prototype-based Interpretable Graph Neural Networks [PDF]
, 2022 Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks.
Biagio La Rosa +2 more
core +1 more source
Towards Deep Attention in Graph Neural Networks: Problems and Remedies [PDF]
, 2023 Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation.
Shin, Kijung +3 more
core +3 more sources
Graph Attention Networks for Speaker Verification [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 5 pages, 1 figure, 2 tables, accepted for presentation at ICASSP 2021 as a conference ...
Jee-weon Jung +3 more
openaire +2 more sources
, 2022 Traffic forecasting is an integral part of intelligent transportation systems (ITS). Achieving a high prediction accuracy is a challenging task due to a high level of dynamics and complex spatial-temporal dependency of road networks. For this task, we
Yang, Song +5 more
core +1 more source
Edge-Featured Graph Attention Network
CoRR, 2021 Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. However, most of these models concentrate on only node features during the learning process. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models.
Jun Chen, Haopeng Chen
openaire +3 more sources
Random graph models for wireless communication networks [PDF]
, 2010 PhDThis thesis concerns mathematical models of wireless communication networks, in particular ad-hoc networks and 802:11 WLANs. In ad-hoc mode each of these devices may function as a sender, a relay or a receiver.
Song, Linlin
core +3 more sources
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 +3 more
openaire +2 more sources
Signed Graph Attention Networks [PDF]
, 2019 Accepted and to appear at ...
Junjie Huang +3 more
openaire +4 more sources
, 2023 Focusing Fine-Grained Action by Self-Attention-Enhanced Graph Neural Networks with Contrastive ...
Lei Lyu (13311267) +4 more
core +1 more source
PointGAT: Graph attention networks for 3D object detection
Intelligent and Converged Networks, 2022 3D object detection is a critical technology in many applications, and among the various detection methods, pointcloud-based methods have been the most popular research topic in recent years. Since Graph Neural Network (GNN) is considered to be effective
Haoran Zhou +3 more
doaj +1 more source