Honghui Chen - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Honghui Chen

Also published under: Hong-Hui Chen, H. Chen, Hong-hui Chen, HongHui Chen

Publications
51
Citations
618
Publications by Year
20062025
Co-Authors:
Show All Co-Authors (73)
Image of author Honghui Chen

Honghui Chen

Also published under: Hong-Hui Chen, H. Chen, Hong-hui Chen, HongHui Chen

Affiliation

Science and Technology on Information Systems Engineering Laboratory
National University of Defense Technology
Changsha, China

Publication Topics

Biography

Honghui Chen received the M.S. degree in operational research and the Ph.D. degree in management science and engineering from the National University of Defense Technology, Changsha, China, in 1994 and 2007, respectively.,He is currently a Professor with the College of System Engineering, National University of Defense Technology. His research interests include information system, cloud computing, and information retrieval.(Based on document published on 11 May 2020).
Publications
51
Citations
618
Publications by Year
20062025
Co-Authors:
Show All Co-Authors (73)
Author's Published Works

Showing 1-25 of 51 results

  • Sort

Filter Results

Show

Sort By

Results

Time-Aware Graph Learning for Link Prediction on Temporal Networks

Zhiqiang Pan;Honghui Chen;Wanyu Chen;Fei Cai;Xinwang Liu

IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Early Access Article |
Link prediction on temporal networks aims to predict the future edges by modeling the dynamic evolution involved in the graph data. Previous methods relying on the node/edge attributes or the distance on the graph structure are not practical due to the deficiency of the attributes and the limitation of the explicit distance estimation, respectively. Moreover, the existing graph representation lear...Show More

Time-Aware Graph Learning for Link Prediction on Temporal Networks

Zhiqiang Pan;Honghui Chen;Wanyu Chen;Fei Cai;Xinwang Liu

Year: 2025 | Early Access Article |

Distance-Aware Learning for Inductive Link Prediction on Temporal Networks

Zhiqiang Pan;Fei Cai;Xinwang Liu;Honghui Chen

IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Volume: 36, Issue: 1 | Journal Article |
Inductive link prediction on temporal networks aims to predict the future links associated with node(s) unseen in the historical timestamps. Existing methods generate the predictions mainly by learning node representation from the node/edge attributes as well as the network dynamics or by measuring the distance between nodes on the temporal network structure. However, the attribute information is ...Show More

Distance-Aware Learning for Inductive Link Prediction on Temporal Networks

Zhiqiang Pan;Fei Cai;Xinwang Liu;Honghui Chen

Year: 2025 | Volume: 36, Issue: 1 | Journal Article |

GMCL: Graph Mask Contrastive Learning for Self-Supervised Graph Representation Learning

Long Xu;Zhiqiang Pan;Honghui Chen;Tianjian Zhou

2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Graph self-supervised learning is a task replete with potential. Previously, mainstream approaches in graph self-supervised learning were based on contrastive or generative tasks to extract graph embeddings, achieving commendable performance in downstream tasks such as node classification. However, these two methods have their respective strengths and limitations: (i) contrastive graph learning ex...Show More

GMCL: Graph Mask Contrastive Learning for Self-Supervised Graph Representation Learning

Long Xu;Zhiqiang Pan;Honghui Chen;Tianjian Zhou

Year: 2024 | Conference Paper |

Correlation-enhanced Dynamic Graph Learning for Temporal Link Prediction

Junzhe Chen;Zhiqiang Pan;Honghui Chen

2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)
Year: 2024 | Conference Paper |
Temporal networks represent the evolving complex systems by regarding the contained elements as nodes and their connections as edges, respectively, which are both time-varying. Link prediction on temporal networks is an essential problem in real-world applications, which aims to forecast the evolution of temporal networks by predicting the future links to appear. However, existing methods generall...Show More

Correlation-enhanced Dynamic Graph Learning for Temporal Link Prediction

Junzhe Chen;Zhiqiang Pan;Honghui Chen

Year: 2024 | Conference Paper |

Multistructure Contrastive Learning for Pretraining Event Representation

Jianming Zheng;Fei Cai;Jun Liu;Yanxiang Ling;Honghui Chen

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 1 | Journal Article |
Cited by: Papers (4)
Event representation aims to transform individual events from a narrative event chain into a set of low-dimensional vectors to help support a series of downstream applications, e.g., similarity differentiation and missing event prediction. Traditional event representation models tend to focus on single modeling perspectives and thus are incapable of capturing physically disconnected yet semantical...Show More

Multistructure Contrastive Learning for Pretraining Event Representation

Jianming Zheng;Fei Cai;Jun Liu;Yanxiang Ling;Honghui Chen

Year: 2024 | Volume: 35, Issue: 1 | Journal Article |

Unveiling the Impact of Network Structure in Enhancing Information Dissemination Efficiency for Military Command and Control : A SIR Modeling Study

Shixian Liu;Miao Jiang;Honghui Chen;Peihong Li;Yanying Mao;Siyuan Wang;Dengfeng Liu

2023 9th International Conference on Big Data and Information Analytics (BigDIA)
Year: 2023 | Conference Paper |
At the intersection of network science, communication models, and military command and control, this study explores the critical impact of network structure on the efficiency of information dissemination. We discuss how these findings can be applied to real-world military situations. Our research employs the SIR model and network simulation to replicate the information propagation process within t...Show More

Unveiling the Impact of Network Structure in Enhancing Information Dissemination Efficiency for Military Command and Control : A SIR Modeling Study

Shixian Liu;Miao Jiang;Honghui Chen;Peihong Li;Yanying Mao;Siyuan Wang;Dengfeng Liu

Year: 2023 | Conference Paper |

Research on Framework of On-demand Resource Matching Based on Deep Semantic Mining

Taihua Shao;Honghui Chen

2023 9th International Conference on Big Data and Information Analytics (BigDIA)
Year: 2023 | Conference Paper |
Resource allocation is pivotal for human society. On-demand resource matching aims to efficiently assign resources to suitable demands based on the resource characteristics, which in widely applicable in area such as e-commerce, recruitment, and military operations. However, conventional matching methods like keyword matching lack semantic analysis, while manual rules have limited adaptability. Wi...Show More

Research on Framework of On-demand Resource Matching Based on Deep Semantic Mining

Taihua Shao;Honghui Chen

Year: 2023 | Conference Paper |

CADer: A Deep Reinforcement Learning Approach for Designing the Communication Architecture of System of Systems

Menglong Lin;Tao Chen;Bangbang Ren;Honghui Chen;Mengmeng Zhang;Deke Guo

IEEE Transactions on Intelligent Vehicles
Year: 2023 | Volume: 8, Issue: 5 | Journal Article |
Cited by: Papers (7)
Modern war relies heavily on various combat units, such as command and control units, surveillance units, and reconnaissance units. These units are usually combined organically as a combat SoS (System of Systems) to conduct given missions. To ensure that the combat SoS executes correctly, the communication systems (e.g., communication vehicles, communication UAVs) must be planned carefully to conn...Show More

CADer: A Deep Reinforcement Learning Approach for Designing the Communication Architecture of System of Systems

Menglong Lin;Tao Chen;Bangbang Ren;Honghui Chen;Mengmeng Zhang;Deke Guo

Year: 2023 | Volume: 8, Issue: 5 | Journal Article |

Keep and Select: Improving Hierarchical Context Modeling for Multi-Turn Response Generation

Yanxiang Ling;Fei Cai;Jun Liu;Honghui Chen;Maarten de Rijke

IEEE Transactions on Neural Networks and Learning Systems
Year: 2023 | Volume: 34, Issue: 7 | Journal Article |
Cited by: Papers (3)
Hierarchical context modeling plays an important role in the response generation for multi-turn conversational systems. Previous methods mainly model context as multiple independent utterances and rely on attention mechanisms to obtain the context representation. They tend to ignore the explicit responds-to relationships between adjacent utterances and the special role that the user’s latest utter...Show More

Keep and Select: Improving Hierarchical Context Modeling for Multi-Turn Response Generation

Yanxiang Ling;Fei Cai;Jun Liu;Honghui Chen;Maarten de Rijke

Year: 2023 | Volume: 34, Issue: 7 | Journal Article |

A Novel Data Placement and Retrieval Service for Cooperative Edge Clouds

Junjie Xie;Chen Qian;Deke Guo;Xin Li;Ge Wang;Honghui Chen

IEEE Transactions on Cloud Computing
Year: 2023 | Volume: 11, Issue: 1 | Journal Article |
Cited by: Papers (10)
Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. These services require short-latency and low-overhead implementation in network and computing devices and load balance on edge ...Show More

A Novel Data Placement and Retrieval Service for Cooperative Edge Clouds

Junjie Xie;Chen Qian;Deke Guo;Xin Li;Ge Wang;Honghui Chen

Year: 2023 | Volume: 11, Issue: 1 | Journal Article |

Topic Enhanced Affective Response Generation in multi-Turn Dialogues

Yanying Mao;Dengfeng Liu;Honghui Chen

2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN)
Year: 2022 | Conference Paper |
Current generation models of emotional dialogue pay more attention to integrating emotion into the process of response generation, but not enough attention to the semantic content of response. In order to enhance the quality of semantic content in response generation, this paper proposes an emotional dialogue generation model incorporating topic information (T-ECCM), which can enhance the relevanc...Show More

Topic Enhanced Affective Response Generation in multi-Turn Dialogues

Yanying Mao;Dengfeng Liu;Honghui Chen

Year: 2022 | Conference Paper |

Exploiting Emotion for Diverse Response Generation in Multi-turn Dialogues

Fei Cai;Yanying Mao;Chengyu Song;Honghui Chen

2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN)
Year: 2022 | Conference Paper |
Generating diversified and affected context-consistent responses is key to intelligent dialogue systems. Previous works about emotional response generation mainly refer to incorporate the emotional category into the decoding process, ignoring the historical emotion in the conversation context. In addition, existing methods mainly fail to consider the diversity of responses and emotional categories...Show More

Exploiting Emotion for Diverse Response Generation in Multi-turn Dialogues

Fei Cai;Yanying Mao;Chengyu Song;Honghui Chen

Year: 2022 | Conference Paper |

COIN: An Efficient Indexing Mechanism for Unstructured Data Sharing Systems

Junjie Xie;Chen Qian;Deke Guo;Minmei Wang;Ge Wang;Honghui Chen

IEEE/ACM Transactions on Networking
Year: 2022 | Volume: 30, Issue: 1 | Journal Article |
Cited by: Papers (11)
Edge computing promises a dramatic reduction in the network latency and the traffic volume, where many edge servers are placed at the edge of the Internet. Furthermore, those edge servers cache data to provide services for edge users. The data sharing among those edge servers can effectively shorten the latency to retrieve the data and further reduce the network bandwidth consumption. The key chal...Show More

COIN: An Efficient Indexing Mechanism for Unstructured Data Sharing Systems

Junjie Xie;Chen Qian;Deke Guo;Minmei Wang;Ge Wang;Honghui Chen

Year: 2022 | Volume: 30, Issue: 1 | Journal Article |

DeepDelivery: Leveraging Deep Reinforcement Learning for Adaptive IoT Service Delivery

Yan Li;Deke Guo;Xiaofeng Cao;Feng Lyu;Honghui Chen

2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)
Year: 2021 | Conference Paper |
To enable fast content delivery for delay-sensitive applications, large content providers build edge servers, Points of Presence (PoPs), and datacenters around the world. They are networked together as an integrated infrastructure via a private wide-area network (WAN), named content delivery network (CDN). To deliver quality services in the CDN, there are two critical decisions that should be prop...Show More

DeepDelivery: Leveraging Deep Reinforcement Learning for Adaptive IoT Service Delivery

Yan Li;Deke Guo;Xiaofeng Cao;Feng Lyu;Honghui Chen

Year: 2021 | Conference Paper |

HDS: A Fast Hybrid Data Location Service for Hierarchical Mobile Edge Computing

Deke Guo;Junjie Xie;Xiaofeng Shi;Haofan Cai;Chen Qian;Honghui Chen

IEEE/ACM Transactions on Networking
Year: 2021 | Volume: 29, Issue: 3 | Journal Article |
Cited by: Papers (14)
The hierarchical mobile edge computing satisfies the stringent latency requirements of data access and processing for emerging edge applications. The data location service is a basic function to provide data storage and retrieval to enable these applications. However, it still lacks research of a scalable and low-latency data location service in the environment. The existing solutions, such as DNS...Show More

HDS: A Fast Hybrid Data Location Service for Hierarchical Mobile Edge Computing

Deke Guo;Junjie Xie;Xiaofeng Shi;Haofan Cai;Chen Qian;Honghui Chen

Year: 2021 | Volume: 29, Issue: 3 | Journal Article |

Efficient Risk-Averse Request Allocation for Multi-Access Edge Computing

Yan Li;Deke Guo;Yawei Zhao;Xiaofeng Cao;Honghui Chen

IEEE Communications Letters
Year: 2021 | Volume: 25, Issue: 2 | Journal Article |
Cited by: Papers (5)
With the evolution of multi-access edge computing (MEC) and 5G communications, diverse services can be flexibly offered by multiple cache-enabled base stations (BSs) in dense deployments. This leads to an important request allocation problem, which studies how to direct user requests among multiple BSs. Efficient request allocation faces several challenges corresponding to the risk associated with...Show More

Efficient Risk-Averse Request Allocation for Multi-Access Edge Computing

Yan Li;Deke Guo;Yawei Zhao;Xiaofeng Cao;Honghui Chen

Year: 2021 | Volume: 25, Issue: 2 | Journal Article |

Exploiting Reliable and Scalable Multicast Services in IaaS Datacenters

Junjie Xie;Deke Guo;Jie Wu;Bangbang Ren;Tao Chen;Honghui Chen

IEEE Transactions on Services Computing
Year: 2021 | Volume: 14, Issue: 5 | Journal Article |
Cited by: Papers (3)
A large number of servers are interconnected using a specific datacenter network to deliver the infrastructure as a service (IaaS). Multicast can jointly utilize the network resources and further reduce the consumption of network bandwidth more than individual unicast. The source of a multicast service, however, does not need to be in a specific location as long as certain constraints are satisfie...Show More

Exploiting Reliable and Scalable Multicast Services in IaaS Datacenters

Junjie Xie;Deke Guo;Jie Wu;Bangbang Ren;Tao Chen;Honghui Chen

Year: 2021 | Volume: 14, Issue: 5 | Journal Article |

A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation

Zhiqiang Pan;Wanyu Chen;Honghui Chen

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation based on user’s current basket, ignoring the inherent preference released by user’s long-term behaviors and fa...Show More

A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation

Zhiqiang Pan;Wanyu Chen;Honghui Chen

Year: 2020 | Volume: 8 | Journal Article |

A Time-Aware Graph Neural Network for Session-Based Recommendation

Yupu Guo;Yanxiang Ling;Honghui Chen

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (6)
Recently, Graph Neural Networks (GNNs) have attracted increasing attention in the field of session-based recommendation, due to its strong ability on capturing complex interactive transitions within sessions. However, existing GNN-based models either lack the use of user's long-term historical behaviors or fail to address the impact of collaborative filtering information from neighbor users on the...Show More

A Time-Aware Graph Neural Network for Session-Based Recommendation

Yupu Guo;Yanxiang Ling;Honghui Chen

Year: 2020 | Volume: 8 | Journal Article |

Fully Projection-Free Proximal Stochastic Gradient Method With Optimal Convergence Rates

Yan Li;Xiaofeng Cao;Honghui Chen

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Proximal stochastic gradient plays an important role in large-scale machine learning and big data analysis. It needs to iteratively update models within a feasible set until convergence. The computational cost is usually high due to the projection over the feasible set. To reduce complexity, many projection-free methods such as Frank-Wolfe methods have been proposed. However, those projection-free...Show More

Fully Projection-Free Proximal Stochastic Gradient Method With Optimal Convergence Rates

Yan Li;Xiaofeng Cao;Honghui Chen

Year: 2020 | Volume: 8 | Journal Article |

A Fast Hybrid Data Sharing Framework for Hierarchical Mobile Edge Computing

Junjie Xie;Deke Guo;Xiaofeng Shi;Haofan Cai;Chen Qian;Honghui Chen

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
Year: 2020 | Conference Paper |
Cited by: Papers (18)
Edge computing satisfies the stringent latency requirements of data access and processing for applications running on edge devices. The data location service is a key function to provide data storage and retrieval to enable these applications. However, it still lacks research of a scalable and low-latency data location service in mobile edge computing. Meanwhile, the existing solutions, such as DN...Show More

A Fast Hybrid Data Sharing Framework for Hierarchical Mobile Edge Computing

Junjie Xie;Deke Guo;Xiaofeng Shi;Haofan Cai;Chen Qian;Honghui Chen

Year: 2020 | Conference Paper |

A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

Yupu Guo;Yanxiang Ling;Honghui Chen

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (7)
Session-aware recommendation is a special form of session-based recommendation, where users' historical interactions before the current session are available. Among the existing session-aware recommendation studies, recurrent neural network (RNN) is a popular choice to model users' current intent of the ongoing session as well as their general preference implied in previous sessions. However, thes...Show More

A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

Yupu Guo;Yanxiang Ling;Honghui Chen

Year: 2020 | Volume: 8 | Journal Article |

Task Scheduling With UAV-Assisted Vehicular Cloud for Road Detection in Highway Scenario

Jiangfan Li;Xiaofeng Cao;Deke Guo;Junjie Xie;Honghui Chen

IEEE Internet of Things Journal
Year: 2020 | Volume: 7, Issue: 8 | Journal Article |
Cited by: Papers (23)
Vehicular cloud computing (VCC) has been utilized to enhance traffic management and road safety. By connecting with base stations (BSes), VCC can provide the information of real-time dynamics for smart vehicles (SVs). However, the area outside the coverage of BSes will be the blind areas, where SVs cannot obtain the real-time safety guarantee, especially on the highway. In this article, we utilize...Show More

Task Scheduling With UAV-Assisted Vehicular Cloud for Road Detection in Highway Scenario

Jiangfan Li;Xiaofeng Cao;Deke Guo;Junjie Xie;Honghui Chen

Year: 2020 | Volume: 7, Issue: 8 | Journal Article |

A Joint Neural Network for Session-Aware Recommendation

Yupu Guo;Duolong Zhang;Yanxiang Ling;Honghui Chen

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (12)
Session-aware recommendation is a special form of sequential recommendation, where users' previous interactions before current session are available. Recently, Recurrent Neural Network (RNN) based models are widely used in sequential recommendation tasks with great success. Previous works mainly focus on the interaction sequences of the current session without analyzing a user's long-term preferen...Show More

A Joint Neural Network for Session-Aware Recommendation

Yupu Guo;Duolong Zhang;Yanxiang Ling;Honghui Chen

Year: 2020 | Volume: 8 | Journal Article |

Minimal Fault-Tolerant Coverage of Controllers in IaaS Datacenters

Junjie Xie;Deke Guo;Xiaomin Zhu;Bangbang Ren;Honghui Chen

IEEE Transactions on Services Computing
Year: 2020 | Volume: 13, Issue: 6 | Journal Article |
Cited by: Papers (8)
Large-scale datacenters are the key infrastructures of cloud computing. Inside a datacenter, a large number of servers are interconnected using a specific datacenter network to deliver the infrastructure as a service (IaaS) for tenants. To realize novel cloud applications like the network virtualization and network isolation among tenants, the principle of software-defined network (SDN) has been a...Show More

Minimal Fault-Tolerant Coverage of Controllers in IaaS Datacenters

Junjie Xie;Deke Guo;Xiaomin Zhu;Bangbang Ren;Honghui Chen

Year: 2020 | Volume: 13, Issue: 6 | Journal Article |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.