Results 21 to 30 of about 1,049,471 (309)
Signed Graph Attention Networks [PDF]
International Conference on Artificial Neural Networks, 2019 Graph or network data is ubiquitous in the real world, including social networks, information networks, traffic networks, biological networks and various technical networks.
Junjie Huang +3 more
semanticscholar +3 more sources
Deep Graph Attention Networks [PDF]
2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW)Graphs are useful for representing various real-world 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 ...
Jun Kato +3 more
semanticscholar +3 more sources
Sparse graphs-based dynamic attention networks
HeliyonIn previous research, the prevailing assumption was that Graph Neural Networks (GNNs) precisely depicted the interconnections among nodes within the graph's architecture. Nonetheless, real-world graph datasets are often rife with noise, elements that can
Runze Chen +4 more
doaj +3 more sources
AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021 Artefacts that differentiate spoofed from bona-fide utterances can reside in specific temporal or spectral intervals. Their reliable detection usually depends upon computationally demanding ensemble systems where each subsystem is tuned to some specific ...
Jee-weon Jung +7 more
semanticscholar +1 more source
End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection [PDF]
2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge, 2021 Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments. Various approaches can be used to capture and model such artefacts, however, none works well across a ...
Hemlata Tak +5 more
semanticscholar +1 more source
Advances in Knowledge Graph Embedding Based on Graph Neural Networks [PDF]
Jisuanji kexue yu tansuo, 2023 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.
YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
doaj +1 more source
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.
Zhang, Yiding +4 more
openaire +2 more sources
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey [PDF]
, 2021 Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also ...
Gabrys, Bogdan +2 more
core +2 more sources
SGAT: Simplicial Graph Attention Network
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Heterogeneous graphs have multiple node and edge types and are semantically richer than homogeneous graphs. To learn such complex semantics, many graph neural network approaches for heterogeneous graphs use metapaths to capture multi-hop interactions between nodes.
Lee, See Hian, Ji, Feng, Tay, Wee Peng
openaire +3 more sources
Heterophily-aware graph attention network
Pattern Recognition, 2023 Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess ...
Junfu Wang +3 more
openaire +2 more sources