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Xiangjie Kong - IEEE Xplore Author Profile

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Author details

Xiangjie Kong

Also published under: Xiang-Jie Kong, Xiang-jie Kong

Publications

104

Citations

4,410

Publications by Year

20092025

Co-Authors:

Ahmedin Mohammed AhmedZafer Al-MakhadmehOsama AlfarrajMohammad AlibakhshikenariNana Yaw Asabere

Show All Co-Authors (227)

Xiangjie Kong

Also published under: Xiang-Jie Kong, Xiang-jie Kong

Affiliation

College of Computer Science and Technology

Zhejiang University of Technology

Hangzhou, China

Publication Topics

Traffic Flow,
Graph Convolutional Network,
Graph Neural Networks,
Anomaly Detection,
Graph Attention Network,
Long Short-term Memory,
Neural Network,
Recurrent Neural Network,
Traffic Data,
Deep Learning,
Deep Reinforcement Learning,
Federated Learning

Biography

Xiangjie Kong (Senior Member, IEEE) received the B.Sc. and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 2004 and 2009, respectively.,He is currently a Full Professor with the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou. He was an Associate Professor with the School of Software, Dalian University of Technology, Dalian, China. He has published over 130 scientific papers in international journals and conferences (with over 100 indexed by ISI SCIE). His research interests include network science, mobile computing, and computational social science.(Based on document published on 14 January 2021).

Publications

104

Citations

4,410

Publications by Year

20092025

Co-Authors:

Ahmedin Mohammed Ahmed
Zafer Al-Makhadmeh
Osama Alfarraj
Mohammad Alibakhshikenari
Nana Yaw Asabere

Show All Co-Authors (227)

Author's Published Works

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Showing 1-25 of 104 results

Journals (73)

Conferences (20)

Magazines (6)

Early Access Articles (5)

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Xiangjie Kong(102)
Feng Xia(48)
Guojiang Shen(35)
Zhaolong Ning(13)
Wei Wang(11)
School of Software, Dalian University of Technology, Dalian, China(39)
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China(39)
Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, China(27)
Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA(5)
School of Engineering, IT and Physical Sciences, Federation University Australia, Ballarat, VIC, Australia(4)
IEEE Access(20)
IEEE Transactions on Industrial Informatics(11)
IEEE Internet of Things Journal(9)
IEEE Transactions on Computational Social Systems(7)
IEEE Transactions on Intelligent Transportation Systems(7)
IEEE(104)
Media(1)
Beijing, China(2)
AB, Canada(1)
Bangkok, Thailand(1)
Chengdu, China(1)
Cologne, Germany(1)
Traffic Flow(23)
Internet Of Things(18)
Graph Convolutional Network(15)
Internet Of Vehicles(14)
Random Walk(14)

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Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System

Xiangjie Kong;Xiaohua He;Xulin Ma;Xiaoran Yan;Lingyun Wang;Guojiang Shen;Zhi Liu

IEEE Transactions on Consumer Electronics

Year: 2025 | Early Access Article |

Federated learning has a wide range of applications in recommendation systems, but most federated recommendation systems can only achieve federated communication between users and servers. Only a few are vertical federated recommendation systems, achieving federated server communication. In addition, the current federated recommendation frameworks require that each participant have the same model....Show More

Oh-FedRec: One-Shot and Heterogeneous Vertical Federated Recommendation System

Xiangjie Kong;Xiaohua He;Xulin Ma;Xiaoran Yan;Lingyun Wang;Guojiang Shen;Zhi Liu

IEEE Transactions on Consumer Electronics

Year: 2025 | Early Access Article |

Model-Heterogeneous Federated Graph Learning With Prototype Propagation

Zhi Liu;Hanlin Zhou;Xiaohua He;Haopeng Yuan;Jiaxin Du;Mengmeng Wang;Guojiang Shen;Xiangjie Kong;Feng Xia

IEEE Transactions on Artificial Intelligence

Year: 2025 | Volume: 6, Issue: 3 | Journal Article |

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Federated graph learning (FGL) enables clients to collaboratively train a robust graph neural network (GNN) while ensuring their private graph data never leaves the local. However, existing FGL frameworks require all clients to train the identical GNN model, which limits their real-world applicability. Although many model-heterogenous frameworks have been proposed for traditional nongraph federate...Show More

Model-Heterogeneous Federated Graph Learning With Prototype Propagation

Zhi Liu;Hanlin Zhou;Xiaohua He;Haopeng Yuan;Jiaxin Du;Mengmeng Wang;Guojiang Shen;Xiangjie Kong;Feng Xia

IEEE Transactions on Artificial Intelligence

Year: 2025 | Volume: 6, Issue: 3 | Journal Article |

Unbiased Anomalous Trajectory Detection with Hierarchical Sequence Modeling

Xiangjie Kong;Yuwei He;Guojiang Shen;Jiaxin Du;Zhi Liu;Ivan Lee

IEEE Transactions on Consumer Electronics

Year: 2024 | Early Access Article |

Anomalous trajectory detection plays an important role in the field of trajectory big data mining, providing significant support for identifying drivers traveling at inappropriate speeds and detecting cab fraud. Current studies often use equal-sized grids to represent trajectory points, and they mainly focus on the general shape of trajectories while ignoring the spatial density distribution of tr...Show More

Unbiased Anomalous Trajectory Detection with Hierarchical Sequence Modeling

Xiangjie Kong;Yuwei He;Guojiang Shen;Jiaxin Du;Zhi Liu;Ivan Lee

IEEE Transactions on Consumer Electronics

Year: 2024 | Early Access Article |

A Comprehensive Survey on Traffic Missing Data Imputation

Yimei Zhang;Xiangjie Kong;Wenfeng Zhou;Jin Liu;Yanjie Fu;Guojiang Shen

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

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Intelligent Transportation Systems (ITS) are essential and play a key role in improving road safety, reducing congestion, optimizing traffic flow and facilitating the development of smart cities. The collection of data from ITS and its transformation into information is challenged by the presence of missing data in datasets. Timely and effective handling of missing data is crucial to facilitate in...Show More

A Comprehensive Survey on Traffic Missing Data Imputation

Yimei Zhang;Xiangjie Kong;Wenfeng Zhou;Jin Liu;Yanjie Fu;Guojiang Shen

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

Vehicular Social Dynamic Anomaly Detection With Recurrent Multi-Mask Aggregator Enabled VAE

Zehao Hu;Yuwei He;Yuqi Shen;Minho Jo;Mario Collotta;Guojiang Shen;Xiangjie Kong

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

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Vehicle driving behavior analysis and detection tasks have become an indispensable part of intelligent transportation systems. Accurate pattern recognition of potential anomalies during the movement of entities is crucial for improving transportation efficiency. Current methods typically analyze vehicle trajectories independently without considering potential interactions among vehicles. To addres...Show More

Vehicular Social Dynamic Anomaly Detection With Recurrent Multi-Mask Aggregator Enabled VAE

Zehao Hu;Yuwei He;Yuqi Shen;Minho Jo;Mario Collotta;Guojiang Shen;Xiangjie Kong

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

Federated Adaptive Pseudo-Labeling Selection for Semi-Supervised Air Writing Recognition Systems

Wenyi Zhang;Xiangjie Kong;Guojiang Shen;Zhenpeng Wu;Youyang Qu

IEEE Transactions on Consumer Electronics

Year: 2024 | Early Access Article |

The rapid advancement of virtual and augmented reality technologies has catalyzed a new paradigm in consumer electronics: air writing, a form of touchless human-computer interaction with vast potential. Implementing air writing recognition systems, however, faces challenges such as label scarcity and privacy concerns. Addressing these, we propose the Federated Adaptive Pseudo-labeling Selection (F...Show More

Federated Adaptive Pseudo-Labeling Selection for Semi-Supervised Air Writing Recognition Systems

Wenyi Zhang;Xiangjie Kong;Guojiang Shen;Zhenpeng Wu;Youyang Qu

IEEE Transactions on Consumer Electronics

Year: 2024 | Early Access Article |

A Diffusion Model for Inductive Knowledge Graph Completion

Zhiyu Chen;Guojiang Shen;Yuqi Shen;Zhi Liu;Xiangjie Kong

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

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Knowledge graph completion (KGC) is dedicated to deducing absent facts within incomplete knowledge graphs (KGs). The majority of antecedent studies exclusively address the transductive scenario, wherein all entities are observed throughout the training process, which is impractical for the everyday reality with constantly emerging entities. Recent works that concern this issue mainly focus on repr...Show More

A Diffusion Model for Inductive Knowledge Graph Completion

Zhiyu Chen;Guojiang Shen;Yuqi Shen;Zhi Liu;Xiangjie Kong

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

An Autonomous Underwater Vehicle-Assisted Adaptive Trust Prediction Model for UASNs

Jiaxin Du;Guangjie Han;Chuan Lin;Xiangjie Kong;Guojiang Shen

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Early Access Article |

Underwater acoustic sensor networks (UASNs) provide significant support for marine intelligent transportation system. As an effective security mechanism, the trust prediction model has been gradually deployed in UASNs to detect anomalous attacks and guarantee the security of marine transportation. However, in existing research on trust models for UASNs, the impact of multidimensional trust feature...Show More

An Autonomous Underwater Vehicle-Assisted Adaptive Trust Prediction Model for UASNs

Jiaxin Du;Guangjie Han;Chuan Lin;Xiangjie Kong;Guojiang Shen

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Early Access Article |

Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Huan Li;Xiangjie Kong;Guojiang Shen;Xiaoran Yan;Yao Yang;Mario Collotta

2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Year: 2024 | Conference Paper |

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Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-...Show More

Point-Correlate Adversarial Transformer for Unsupervised Multivariate Time Series Anomaly Detection

Huan Li;Xiangjie Kong;Guojiang Shen;Xiaoran Yan;Yao Yang;Mario Collotta

2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Year: 2024 | Conference Paper |

Deep Trajectory Recovery Approach of Offline Vehicles in the Internet of Vehicles

Xiao Han;Ding-Xuan Zhou;Guojiang Shen;Xiangjie Kong;Yulong Zhao

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 11 | Journal Article |

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Multi-modal trajectory analysis and trajectory recovery are essential tasks in transportation research, especially for offline vehicles, which enable comprehensive understanding of complex transportation systems and address the issue of incomplete or missing trajectory data. In this paper, we propose a novel Deep Trajectory Recovery Framework, DTRF, which can effectively tackle both challenges by ...Show More

Deep Trajectory Recovery Approach of Offline Vehicles in the Internet of Vehicles

Xiao Han;Ding-Xuan Zhou;Guojiang Shen;Xiangjie Kong;Yulong Zhao

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 11 | Journal Article |

Asynchronous Federated Deep-Reinforcement-Learning-Based Dependency Task Offloading for UAV-Assisted Vehicular Networks

Si Shen;Guojiang Shen;Zhehao Dai;Kaiyu Zhang;Xiangjie Kong;Jianxin Li

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 19 | Journal Article |

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Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a valuable supplement to conventional MEC, offering unique advantages in temporary or emergency scenarios. However, the integration of UAVs into MEC introduces new challenges. First, the existing UAV-empowered MEC frameworks predominantly have not accounted for dependencies between the computing tasks. Second, the li...Show More

Asynchronous Federated Deep-Reinforcement-Learning-Based Dependency Task Offloading for UAV-Assisted Vehicular Networks

Si Shen;Guojiang Shen;Zhehao Dai;Kaiyu Zhang;Xiangjie Kong;Jianxin Li

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 19 | Journal Article |

Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning

Xiangjie Kong;Wenyi Zhang;Hui Wang;Mingliang Hou;Xin Chen;Xiaoran Yan;Sajal K. Das

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Early Access Article |

Cited by: Papers (4)

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain...Show More

Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning

Xiangjie Kong;Wenyi Zhang;Hui Wang;Mingliang Hou;Xin Chen;Xiaoran Yan;Sajal K. Das

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Early Access Article |

KGNext: Knowledge-Graph-Enhanced Transformer for Next POI Recommendation With Uncertain Check-Ins

Xiangjie Kong;Zhiyu Chen;Jianxin Li;Junhui Bi;Guojiang Shen

IEEE Transactions on Computational Social Systems

Year: 2024 | Volume: 11, Issue: 5 | Journal Article |

Cited by: Papers (1)

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The next point-of-interest (POI) recommendation aims to predict users’ future movements based on their historical trajectories. However, in reality, users may provide uncertain check-in records, resulting in uploaded data that lack precise location information and is instead ambiguous. Despite this challenge, only a limited number of studies have addressed this issue, often overlooking the intrica...Show More

KGNext: Knowledge-Graph-Enhanced Transformer for Next POI Recommendation With Uncertain Check-Ins

Xiangjie Kong;Zhiyu Chen;Jianxin Li;Junhui Bi;Guojiang Shen

IEEE Transactions on Computational Social Systems

Year: 2024 | Volume: 11, Issue: 5 | Journal Article |

Anomalous Sub-Trajectory Detection With Graph Contrastive Self-Supervised Learning

Xiangjie Kong;Hang Lin;Renhe Jiang;Guojiang Shen

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 7 | Journal Article |

Cited by: Papers (5)

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Some anomalous vehicle trajectories may contain fraudulent behavior or traffic accident information. Existing research mostly starts from a global view, treating the entire trajectory as a detection target to determine whether it is anomalous. However, they failed to fully capture the influence of sub-trajectories on the entire trajectory, thus ignoring some anomalous sub-trajectories. This study ...Show More

Anomalous Sub-Trajectory Detection With Graph Contrastive Self-Supervised Learning

Xiangjie Kong;Hang Lin;Renhe Jiang;Guojiang Shen

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 7 | Journal Article |

Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions

Xiangjie Kong;Juntao Wang;Zehao Hu;Yuwei He;Xiangyu Zhao;Guojiang Shen

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 11 | Journal Article |

Cited by: Papers (2)

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The growing number of cars on city roads has led to an increase in traffic accidents, highlighting the need for traffic safety measures. Mobile trajectory anomaly detection is an important area of research that can identify unusual patterns or trajectories in urban environments and provide timely warnings to drivers to avoid accidents. However, there is a significant lack of research on the analys...Show More

Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions

Xiangjie Kong;Juntao Wang;Zehao Hu;Yuwei He;Xiangyu Zhao;Guojiang Shen

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 11 | Journal Article |

Energy Consumption Optimization of UAV-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning

Xiangjie Kong;Chenhao Ni;Gaohui Duan;Guojiang Shen;Yao Yang;Sajal K. Das

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 12 | Journal Article |

Cited by: Papers (7)

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Unmanned aerial vehicles (UAVs) equipped with high-definition cameras have the capability to capture comprehensive and multiangled images of road conditions, facilitating more efficient collection of pertinent road data. However, drones encounter challenges in performing related tasks for an extended period due to their limited energy capacity. Therefore, a crucial concern is how to plan the path ...Show More

Energy Consumption Optimization of UAV-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning

Xiangjie Kong;Chenhao Ni;Gaohui Duan;Guojiang Shen;Yao Yang;Sajal K. Das

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 12 | Journal Article |

Exploring Bus Stop Mobility Pattern: A Multi-Pattern Deep Learning Prediction Framework

Xiangjie Kong;Zhehui Shen;Kailai Wang;Guojiang Shen;Yanjie Fu

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 7 | Journal Article |

Cited by: Papers (7)

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The spatio-temporal prediction task in the transportation network is the core of the solutions for various traffic problems. On one hand, the mobility pattern in traffic can be reflected in the travel behavior of the crowd. In most traffic prediction tasks, the importance of the mobility pattern is often overlooked. On the other hand, traffic prediction also has a variety of predicting scenarios, ...Show More

Exploring Bus Stop Mobility Pattern: A Multi-Pattern Deep Learning Prediction Framework

Xiangjie Kong;Zhehui Shen;Kailai Wang;Guojiang Shen;Yanjie Fu

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 7 | Journal Article |

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

Chhaya Gupta;Nasib Singh Gill;Preeti Gulia;Sangeeta Yadav;Giovanni Pau;Mohammad Alibakhshikenari;Xiangjie Kong

IEEE Open Journal of the Computer Society

Year: 2024 | Volume: 5 | Journal Article |

Cited by: Papers (6)

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Computer vision technologies have greatly improved in the last few years. Many problems have been solved using deep learning merged with more computational power. Action recognition is one of society's problems that must be addressed. Human Action Recognition (HAR) may be adopted for intelligent video surveillance systems, and the government may use the same for monitoring crimes and security purp...Show More

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

Chhaya Gupta;Nasib Singh Gill;Preeti Gulia;Sangeeta Yadav;Giovanni Pau;Mohammad Alibakhshikenari;Xiangjie Kong

IEEE Open Journal of the Computer Society

Year: 2024 | Volume: 5 | Journal Article |

FedAWR: An Interactive Federated Active Learning Framework for Air Writing Recognition

Xiangjie Kong;Wenyi Zhang;Youyang Qu;Xinwei Yao;Guojiang Shen

IEEE Transactions on Mobile Computing

Year: 2024 | Volume: 23, Issue: 5 | Journal Article |

Cited by: Papers (6)

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The rapid development of technology such as virtual reality and augmented reality, coupled with the reduced direct contact due to the COVID-19 pandemic, has led to the emergence of a more advanced mode of interaction: air handwriting. This new form of human-computer interaction allows users to input text by writing in the air freely. However, deploying and applying existing air handwriting recogni...Show More

FedAWR: An Interactive Federated Active Learning Framework for Air Writing Recognition

Xiangjie Kong;Wenyi Zhang;Youyang Qu;Xinwei Yao;Guojiang Shen

IEEE Transactions on Mobile Computing

Year: 2024 | Volume: 23, Issue: 5 | Journal Article |

UMIM: Utility-Maximization Incentive Mechanism for Mobile Crowd Sensing

Xin-Wei Yao;Xiao-Tian Yang;Qiang Li;Chu-Feng Qi;Xiang-Jie Kong;Xiang-Yang Li

IEEE Transactions on Mobile Computing

Year: 2024 | Volume: 23, Issue: 5 | Journal Article |

Cited by: Papers (5)

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Mobile Crowd Sensing (MCS) represents a novel paradigm which utilizes intelligent devices carried by mobile users to collect and transmit data. Appropriate incentives are essential to recruit enough participants for sensing tasks. Existing works have designed some incentive mechanisms for MCS, which are not suitable for scenarios when the participants increase significantly as a result of the boom...Show More

UMIM: Utility-Maximization Incentive Mechanism for Mobile Crowd Sensing

Xin-Wei Yao;Xiao-Tian Yang;Qiang Li;Chu-Feng Qi;Xiang-Jie Kong;Xiang-Yang Li

IEEE Transactions on Mobile Computing

Year: 2024 | Volume: 23, Issue: 5 | Journal Article |

MissII: Missing Information Imputation for Traffic Data

Mingliang Hou;Tao Tang;Feng Xia;Ibrahim Sultan;Roopdeep Kaur;Xiangjie Kong

IEEE Transactions on Emerging Topics in Computing

Year: 2024 | Volume: 12, Issue: 3 | Journal Article |

Cited by: Papers (4)

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Cyber-Physical-Social Systems (CPSS) offer a new perspective for applying advanced information technology to improve urban transportation. However, real-world traffic datasets collected from sensing devices like loop sensors often contain corrupted or missing values. The incompleteness of traffic data poses great challenges to downstream data analysis tasks and applications. Most existing data-dri...Show More

MissII: Missing Information Imputation for Traffic Data

Mingliang Hou;Tao Tang;Feng Xia;Ibrahim Sultan;Roopdeep Kaur;Xiangjie Kong

IEEE Transactions on Emerging Topics in Computing

Year: 2024 | Volume: 12, Issue: 3 | Journal Article |

Urban Overtourism Detection Based on Graph Temporal Convolutional Networks

Xiangjie Kong;Zhiqiang Huang;Guojiang Shen;Hang Lin;Mingjie Lv

IEEE Transactions on Computational Social Systems

Year: 2024 | Volume: 11, Issue: 1 | Journal Article |

Cited by: Papers (3)

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Urban overtourism results in heavy traffic, degraded tourist experiences, and overloaded infrastructure. Detecting urban overtourism at the early stage is important to minimize the adverse effects. However, urban overtourism detection (UOD) is a challenging task due to ambiguity, sparsity, and complex spatiotemporal relations of overtourism. In this article, we propose a novel UOD framework based ...Show More

Urban Overtourism Detection Based on Graph Temporal Convolutional Networks

Xiangjie Kong;Zhiqiang Huang;Guojiang Shen;Hang Lin;Mingjie Lv

IEEE Transactions on Computational Social Systems

Year: 2024 | Volume: 11, Issue: 1 | Journal Article |

Spatio-Temporal Digraph Convolutional Network-Based Taxi Pickup Location Recommendation

Yan Zhang;Guojiang Shen;Xiao Han;Wei Wang;Xiangjie Kong

IEEE Transactions on Industrial Informatics

Year: 2023 | Volume: 19, Issue: 1 | Journal Article |

Cited by: Papers (12)

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The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional net...Show More

Spatio-Temporal Digraph Convolutional Network-Based Taxi Pickup Location Recommendation

Yan Zhang;Guojiang Shen;Xiao Han;Wei Wang;Xiangjie Kong

IEEE Transactions on Industrial Informatics

Year: 2023 | Volume: 19, Issue: 1 | Journal Article |

CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs

Feng Xia;Lei Wang;Tao Tang;Xin Chen;Xiangjie Kong;Giles Oatley;Irwin King

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 5 | Journal Article |

Cited by: Papers (14)

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Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominan...Show More

CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs

Feng Xia;Lei Wang;Tao Tang;Xin Chen;Xiangjie Kong;Giles Oatley;Irwin King

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 5 | Journal Article |

Edge Computing for Internet of Everything: A Survey

Xiangjie Kong;Yuhan Wu;Hui Wang;Feng Xia

IEEE Internet of Things Journal

Year: 2022 | Volume: 9, Issue: 23 | Journal Article |

Cited by: Papers (110)

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In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy...Show More

Edge Computing for Internet of Everything: A Survey

Xiangjie Kong;Yuhan Wu;Hui Wang;Feng Xia

IEEE Internet of Things Journal

Year: 2022 | Volume: 9, Issue: 23 | Journal Article |

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