Results 41 to 50 of about 4,082,283 (306)

Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Data Science and Engineering, 2023
Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li, Man-Sheng Chen, Yuefang Gao, Chang-Dong Wang   +3 more
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MBHAN: Motif-Based Heterogeneous Graph Attention Network

Applied Sciences, 2022
Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Qian Hu, Weiping Lin, Minli Tang, Jiatao Jiang   +3 more
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Advances in Graph Neural Networks for Combinatorial Optimization Problems [PDF]

Jisuanji kexue yu tansuo
Combinatorial optimization, as an important branch of mathematical optimization, focuses on finding optimal solution within a finite discrete solution space.
ZHU Ye, DING Cangfeng, CAO Bohao, CHEN Kexin
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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
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Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network

Sensors, 2023
In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks ...
Dong Wang, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang, Yan Zhu   +4 more
doaj   +1 more source

Graph Neural Networks for Graph Search [PDF]

Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 2020
Graph neural networks (GNNs) have received more and more attention in past several years, due to the wide applications of graphs and networks, and the superiority of their performance compared to traditional heuristics-driven approaches. However, most existing GNNs still focus on node-level applications, such as node classification and link prediction,
openaire   +2 more sources

Grammars and cellular automata for evolving neural networks architectures [PDF]

, 2000
IEEE International Conference on Systems, Man, and Cybernetics. Nashville, TN, 8-11 October 2000The class of feedforward neural networks trained with back-propagation admits a large variety of specific architectures applicable to approximation pattern ...
Molina López, José Manuel, Galván, Inés M., Sanchis de Miguel, María Araceli, Isasi, Pedro   +3 more
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Rethinking Graph Regularization for Graph Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model f(X). However, with the recent popularity of graph neural networks (GNNs), directly encoding graph structure A into a model, i.e., f(A, X), has become the more common approach.
Han Yang 0002, Kaili Ma 0001, James Cheng   +2 more
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Lazy training of radial basis neural networks [PDF]

, 2006
Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space.
Galván, Inés M., Inés M. Galván, Pedro Isasi, José M. Valls, Valls, José M., Isasi, Pedro   +5 more
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The Graph Neural Network Model

IEEE Transactions on Neural Networks, 2009
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural ...
SCARSELLI, FRANCO, GORI, MARCO, TSOI A. C, HAGENBUCHNER M, MONFARDINI, GABRIELE   +4 more
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