Results 1 to 10 of about 4,958,857 (246)
DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting [PDF]
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
exaly +5 more sources
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
exaly +3 more sources
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
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
openaire +3 more sources
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
doaj +1 more source
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
doaj +1 more source
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring.
Kun Chen +5 more
doaj +1 more source
IA-GCN: Interactive Graph Convolutional Network for Recommendation
CoRR, 2022 Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on a user-item bipartite graph, and then provide the users with personalized item suggestions based on
Zhang, Yinan +9 more
openaire +3 more sources