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PT-TDGCN: Pre-Trained Trend-Aware Dynamic Graph Convolutional Network for Traffic Flow Prediction [PDF]

Sensors
Accurate traffic flow prediction is vital for intelligent transportation systems, yet strong spatiotemporal coupling and multi-scale dynamics make modelling difficult.
Hanqing Yang, Sen Wei, Yuanqing Wang
doaj   +2 more sources

Medical irregular multivariate time series forecasting based on multi-scale temporal-frequency domain patch fusion and dynamic graph [PDF]

Frontiers in Physiology
IntroductionAccurate forecasting of medical irregular multivariate time series is an important prerequisite for downstream monitoring and decision-support research.
Xueping Liu, Tianyi Gong, Youru Li, Na Li, Silu Ding   +4 more
doaj   +2 more sources

Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

IEEE Access, 2021
Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also ...
Joakim Skarding, Bogdan Gabrys, Katarzyna Musial   +2 more
doaj   +3 more sources

Dynamic Graph Representation Learning With Neural Networks: A Survey

IEEE Access
In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation.
Leshanshui Yang, Clement Chatelain, Sebastien Adam   +2 more
doaj   +3 more sources

Dynamic graph learning framework based seasonal and trend decomposition approach for potato crop evapotranspiration prediction [PDF]

Scientific Reports
Efficient estimation of crop water requirements (ETc) is important for sustainable agricultural water management, particularly under increasing climate variability.
Saad Javed Cheema, Mohammed Diykh, Mumtaz Ali, Aitazaz A. Farooque, Raheleh Malekian, Shoaib Rashid Saleem, Lakshman W. Galagedara, Rehan Sadiq, Gurjit S. Randhawa, Qamar Uz Zaman   +9 more
doaj   +2 more sources

DGTR: Dynamic graph transformer for rumor detection

Frontiers in Research Metrics and Analytics, 2023
Social media rumors have the capacity to harm the public perception and the social progress. The news propagation pattern is a key clue for detecting rumors.
Siqi Wei, Bin Wu, Aoxue Xiang, Yangfu Zhu, Chenguang Song   +4 more
doaj   +1 more source

Taxi origin and destination demand prediction based on deep learning: a review

Digital Transportation and Safety, 2023
Taxi demand prediction is a crucial component of intelligent transportation system research. Compared to region-based demand prediction, origin-destination (OD) demand prediction has a wide range of potential applications, including real-time matching ...
Dan Peng, Mingxia Huang, Zhibo Xing
doaj   +1 more source

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

Sensors, 2020
Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference ...
Di Zhao, Hao Qin, Bin Song, Beichen Han, Xiaojiang Du, Mohsen Guizani   +5 more
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Distributed Truss Computation in Dynamic Graphs

Tsinghua Science and Technology, 2023
Large-scale graphs usually exhibit global sparsity with local cohesiveness, and mining the representative cohesive subgraphs is a fundamental problem in graph analysis.
Ziwei Mo, Qi Luo, Dongxiao Yu, Hao Sheng, Jiguo Yu, Xiuzhen Cheng   +5 more
doaj   +1 more source

Anomaly Detection by Learning Dynamics From a Graph

IEEE Access, 2020
There exist relations, which vary with time or by an event, between high dimensional elements in most real-world datasets. A dynamic graph or network has been used as one of the remarkable approaches to represent and analyze them.
Jaekoo Lee, Ho Bae, Sungroh Yoon
doaj   +1 more source