Results 321 to 330 of about 1,903,201 (339)

A Graph Neural Network-Based Digital Twin for Network Slicing Management

IEEE Transactions on Industrial Informatics, 2022
Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized ...
Haozhe Wang, Yulei Wu, G. Min, W. Miao
semanticscholar   +1 more source

NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs

Knowledge Discovery and Data Mining, 2021
Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-world graphs are often sparsely and noisily labeled, which could ...
Enyan Dai, C. Aggarwal, Suhang Wang
semanticscholar   +1 more source

Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction

Briefings Bioinform., 2023
Drug-target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data.
Mei Li, Xiangrui Cai, Sihan Xu, Hua Ji
semanticscholar   +1 more source

Heterogeneous Graph Neural Network via Attribute Completion

The Web Conference, 2021
Heterogeneous information networks (HINs), also called heterogeneous graphs, are composed of multiple types of nodes and edges, and contain comprehensive information and rich semantics. Graph neural networks (GNNs), as powerful tools for graph data, have
Di Jin, Cuiying Huo, Chundong Liang, Liang Yang   +3 more
semanticscholar   +1 more source

Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection

IEEE Transactions on Neural Networks and Learning Systems
The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services.
Xiaokang Zhou, Jiayi Wu, Wei Liang, K. Wang, Zheng Yan, Laurence T. Yang, Qun Jin   +6 more
semanticscholar   +1 more source

Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks

2023
Neural networks that can process the parameters of other neural networks find applications in diverse domains, including processing implicit neural representations, domain adaptation of pretrained networks, generating neural network weights, and predicting generalization errors.
Zhang, D.W., Kofinas, M., Zhang, Y., Chen, Y., Burghouts, G.J., Snoek, C.G.M.   +5 more
openaire   +1 more source

Graph Ensemble Neural Network

Information Fusion, 2023
Rui Duan, Chungang Yan, Junli Wang, Changjun Jiang   +3 more
openaire   +1 more source

Graph Neural Networks : Graph Representation Learning using Neural Networks

This book, "Graph Neural Networks: Graph Representation Learning with a Neural Network Approach," offers a comprehensive and up-to-date guide to the dynamic field of Graph Neural Networks (GNNs). It serves as an essential Persian-language resource for students, researchers, and AI practitioners.
Boreiri, Zahra, Moeini, Ali, Abedian, Rooholah   +2 more
openaire   +1 more source

Graph Neural Networks

2023
Yinhai Wang, Zhiyong Cui, Ruimin Ke
  +4 more sources

A survey of graph neural network based recommendation in social networks

Neurocomputing, 2023
Xiao Li, Li Sun, Mengjie Ling, Yan Peng
semanticscholar   +1 more source