Results 31 to 40 of about 1,708,308 (346)
RG-GCN: Improved Graph Convolution Neural Network Algorithm Based on Rough Graph
IEEE Access, 2022 The graph convolution neural network uses topological graph to portray inter-node relationships and update node features. However, the traditional topological graph can only describe the certain relationship between nodes (that is, the weight of the ...
Weiping Ding +7 more
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Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting [PDF]
Proceedings of the VLDB Endowment, 2022 We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task.
Zezhi Shao +6 more
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Graph Neural Network Defense Combined with Contrastive Learning [PDF]
Jisuanji kexue yu tansuo, 2023 Although graph neural networks have achieved good performance in the field of graph representation learning, recent studies have shown that graph neural networks are more susceptible to adversarial attacks on graph structure, namely, by adding well ...
CHEN Na, HUANG Jincheng, LI Ping
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Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [PDF]
Computer Vision and Pattern Recognition, 2020 In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the
Weijing Shi, R. Rajkumar
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Power System Network Topology Identification Based on Knowledge Graph and Graph Neural Network
Frontiers in Energy Research, 2021 The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their ...
Changgang Wang, Jun An, Jun An, Gang Mu
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Graph Clustering with Graph Neural Networks
J. Mach. Learn. Res., 2020 Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs.
Anton Tsitsulin +3 more
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A federated graph neural network framework for privacy-preserving personalization [PDF]
Nature Communications, 2021 Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation.
Chuhan Wu +4 more
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Graph Rewriting for Graph Neural Networks
, 2023 Originally submitted to ICGT 2023, part of STAF ...
Adam Machowczyk, Reiko Heckel
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Learning graph normalization for graph neural networks [PDF]
Neurocomputing, 2022 15 pages, 3 figures, 6 ...
Yihao Chen +4 more
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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
semanticscholar +1 more source