Results 1 to 10 of about 7,335 (204)
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction [PDF]
IEEE Transactions on Intelligent Transportation Systems, 2020 Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and ...
, Yu Liu, Haifeng Li
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A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting [PDF]
ISPRS International Journal of Geo-Information, 2021 Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked
Haifeng Li, Jiawei Zhu, Ling Zhao
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DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting
Transportation Research Part C: Emerging Technologies, 2022 Traffic speed forecasting is one of the core problems in transportation systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks.
Wonjong Rhee
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MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction
IEEE Access, 2019 Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data. In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by
Jiaming Huang +2 more
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Graph Convolutional Network with Adaptive Fusion of Neighborhood Aggregation and Interaction [PDF]
Jisuanji kexue yu tansuo, 2023 Graph representation learning technology aims to learn the low dimensional representation vectors for nodes while maintaining the properties of graphs and provide materials for downstream tasks.
FU Kun, ZHUO Jiaming, GUO Yunpeng, LI Jianing, LIU Qi
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Bi-GCN: Binary Graph Convolutional Network [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Accepted by CVPR 2021 as oral ...
Junfu Wang +4 more
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SPATIOTEMPORAL GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR METRO FLOW PREDICTION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Forecasting urban metro flow accurately plays an important role for station management and passenger safety. Owing to the limitations of non-linearity and complexity of traffic flow data, traditional methods cannot satisfy the requirements of effectively
S. Jin, C. Jing, Y. Wang, X. Lv
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GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks [PDF]
ACM Transactions on Graphics, 2022 In this article, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks ( GCNs ). Unlike previous learning-based mesh denoising methods that exploit handcrafted or voxel-based representations for feature learning, our method explores ...
Yuefan Shen +7 more
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Novel Graph Convolutional Network Based on Multi-granularity Feature Fusion for Aspect-basedSentiment Analysis [PDF]
Jisuanji kexue, 2023 Aspect-based sentiment analysis(ABSA) is a fine-grained task in sentiment analysis that aims to detect the emotional polarity of aspects in given sentence.Due to the rise of deep learning and graph convolutional networks(GCNs),GCN constructed over ...
DENG Ruhan, ZHANG Qinghua, HUANG Shuaishuai, GAO Man
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Leak Detection in Water Supply Network Using a Data-Driven Improved Graph Convolutional Network
IEEE Access, 2023 Due to the complex correlation within data collection, it is a challenging task to detect leakage in the water supply network. The Graph Convolutional Network (GCN) has recently gained significant attention in correlation research. However, most existing
Suisheng Chen +4 more
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