Results 31 to 40 of about 1,903,201 (339)
Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning
IEEE Transactions on Industrial Informatics, 2023 Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be ...
Wen Song +3 more
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Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power
Giorgos Bouritsas +3 more
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DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation [PDF]
Web Search and Data Mining, 2022 Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
Liangwei Yang +6 more
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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 +7 more
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Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification
Defence Technology, 2023 With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult Problem in current research. The graph neural network (GNN) has emerged as an approach to semi-supervised classification, and the application of GNN to ...
Ding Yao +6 more
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Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider both complex intra-variable dependencies and inter-variable dependencies ...
Ling Chen +6 more
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Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data
Mathematics, 2023 Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets.
Anna Boronina +2 more
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Intelligent prediction method of network performance based on graph neural network
Dianxin kexue, 2022 There are some problems in the traditional network performance prediction technology, such as incomplete network state acquisition and poor accuracy of network performance evaluation.Combined with the characteristics of graph neural network learning and ...
Yijiang LI +5 more
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Frontiers in Cell and Developmental Biology, 2021 Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight.
Vidya Manian +2 more
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Graph Neural Network for Traffic Forecasting: The Research Progress
ISPRS Int. J. Geo Inf., 2023 Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the
Weiwei Jiang +3 more
semanticscholar +1 more source