Results 11 to 20 of about 409,466 (266)

Graph Neural Networks in Network Neuroscience

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph.
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik   +2 more
openaire   +6 more sources

A Review of Graph Neural Networks and Their Applications in Power Systems

Journal of Modern Power Systems and Clean Energy, 2022
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains.
Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang   +4 more
doaj   +3 more sources

Curvature graph neural network [PDF]

Information Sciences, 2022
Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables measuring the importance of neighboring nodes to the target node by a node-specific mechanism.
Li, Haifeng, Cao, Jun, Zhu, Jiawei, Liu, Yu, Zhu, Qing, Wu, Guohua   +5 more
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Binarized graph neural network [PDF]

World Wide Web, 2021
Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based ...
Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xiangjian He, Yiguang Lin, Xuemin Lin   +6 more
openaire   +2 more sources

Stochastic Graph Neural Networks [PDF]

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
openaire   +2 more sources

Graph Neural Network Bandits

Advances in Neural Information Processing Systems 35, 2022
We consider the bandit optimization problem with the reward function defined over graph-structured data. This problem has important applications in molecule design and drug discovery, where the reward is naturally invariant to graph permutations. The key challenges in this setting are scaling to large domains, and to graphs with many nodes.
Kassraie, Parnian, Krause, Andreas, Bogunovic, Ilija   +2 more
openaire   +3 more sources

Graph neural network [PDF]

SCIENTIA SINICA Mathematica, 2020
Summary: In recent years, with the emergence of massive data, graph structure data that can represent complex relationships between objects has received more and more attention and has brought great challenges to existing algorithms. As a deep topology information can be revealed, graph neural network models have been widely used in many fields such as
Bai, Bo, Liu, Yuting, Ma, Chicheng, Wang, Guanghui, Yan, Guiying, Yan, Kai, Zhang, Ming, Zhou, Zhiheng   +7 more
openaire   +1 more source

Factor Graph Neural Networks

, 2023
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs.
Zhang, Zhen, Dupty, Mohammed Haroon, Wu, Fan, Shi, Javen Qinfeng, Lee, Wee Sun   +4 more
openaire   +3 more sources

Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]

Jisuanji kexue
Node classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
doaj   +1 more source

k-hop graph neural networks

Neural Networks, 2020
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler-Leman test of graph isomorphism in terms of distinguishing non-isomorphic graphs.
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis   +2 more
openaire   +5 more sources