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Guoren Wang - IEEE Xplore Author Profile
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Guoren Wang

Publications
189
Citations
1,524
Publications by Year
20062025
Co-Authors:
Show All Co-Authors (325)
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Guoren Wang

Affiliation

School of Computer Science and Technology, Beijing Institute of Technology, China

Publication Topics

Biography

Guoren Wang received the BSc, MSc, and PhD degrees from the Department of Computer Science, Northeastern University, Shenyang, China, in 1988, 1991, and 1996, respectively. Currently, he is a professor with the Department of Computer Science, Beijing Institute of Technology, Beijing, China. His research interests include XML data management, query processing and optimization, bioinformatics, high dimensional indexing, parallel database systems, and cloud data management. He has published more than 100 research papers.(Based on document published on 6 October 2021).
Publications
189
Citations
1,524
Publications by Year
20062025
Co-Authors:
Show All Co-Authors (325)
Author's Published Works

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Shared Growth of Graph Neural Networks Via Prompted Free-Direction Knowledge Distillation

Kaituo Feng;Yikun Miao;Changsheng Li;Ye Yuan;Guoren Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is often quite challenging to train a satisfactory deeper GNN due to the well-known over-parametrized and over-smoothing issues, leading to invalid knowledge transfer i...Show More

Shared Growth of Graph Neural Networks Via Prompted Free-Direction Knowledge Distillation

Kaituo Feng;Yikun Miao;Changsheng Li;Ye Yuan;Guoren Wang

Year: 2025 | Early Access Article |

Loci: Federated Continual Learning of Heterogeneous Tasks at Edge

Yaxin Luopan;Rui Han;Qinglong Zhang;Xiaojiang Zuo;Chi Harold Liu;Guoren Wang;Lydia Y. Chen

IEEE Transactions on Parallel and Distributed Systems
Year: 2025 | Volume: 36, Issue: 4 | Journal Article |
Federated continual learning (FCL) has attracted growing attention in achieving collaborative model training among edge clients, each of which learns its local model for a sequence of tasks. Most existing FCL approaches aggregate clients’ latest local models to exchange knowledge. This unfortunately deviates from real-world scenarios where each model is optimized independently using the client’s o...Show More

Loci: Federated Continual Learning of Heterogeneous Tasks at Edge

Yaxin Luopan;Rui Han;Qinglong Zhang;Xiaojiang Zuo;Chi Harold Liu;Guoren Wang;Lydia Y. Chen

Year: 2025 | Volume: 36, Issue: 4 | Journal Article |

Colorful Star Motif Counting: Concepts, Algorithms and Applications

Hongchao Qin;Gao Sen;Rong-Hua Li;Hongzhi Chen;Ye Yuan;Guoren Wang

IEEE Transactions on Knowledge and Data Engineering
Year: 2025 | Volume: 37, Issue: 3 | Journal Article |
A colorful star motif is a star-shaped graph where any two nodes have different colors. Counting the colorful star motif can help to analyze the structural properties of real-life colorful graphs, model higher-order clustering, and accelerate the mining of the densest subgraph exhibiting $h$h-clique characteristics in graphs. In this manuscript, we introduce the concept of colorful $h$h-star in a ...Show More

Colorful Star Motif Counting: Concepts, Algorithms and Applications

Hongchao Qin;Gao Sen;Rong-Hua Li;Hongzhi Chen;Ye Yuan;Guoren Wang

Year: 2025 | Volume: 37, Issue: 3 | Journal Article |

HAda: Hyper-Adaptive Parameter-Efficient Learning for Multi-View ConvNets

Shiye Wang;Changsheng Li;Zeyu Yan;Wanjun Liang;Ye Yuan;Guoren Wang

IEEE Transactions on Image Processing
Year: 2025 | Volume: 34 | Journal Article |
Recent years have witnessed a great success of multi-view learning empowered by deep ConvNets, leveraging a large number of network parameters. Nevertheless, there is an ongoing consideration regarding the essentiality of all these parameters in multi-view ConvNets. As we know, hypernetworks offer a promising solution to reduce the number of parameters by learning a concise network to generate wei...Show More

HAda: Hyper-Adaptive Parameter-Efficient Learning for Multi-View ConvNets

Shiye Wang;Changsheng Li;Zeyu Yan;Wanjun Liang;Ye Yuan;Guoren Wang

Year: 2025 | Volume: 34 | Journal Article |

EdgeTA: Neuron-Grained Scaling of Foundation Models in Edge-Side Retraining

Qinglong Zhang;Rui Han;Chi Harold Liu;Guoren Wang;Song Guo;Lydia Y. Chen

IEEE Transactions on Mobile Computing
Year: 2025 | Volume: 24, Issue: 4 | Journal Article |
Foundation models (FMs) such as large language models are becoming the backbone technology for artificial intelligence systems. It is particularly challenging to deploy multiple FMs on edge devices, which not only have limited computational resources, but also encounter unseen input data from evolving domains or learning tasks. When new data arrives, existing prior art of FM mainly focuses on retr...Show More

EdgeTA: Neuron-Grained Scaling of Foundation Models in Edge-Side Retraining

Qinglong Zhang;Rui Han;Chi Harold Liu;Guoren Wang;Song Guo;Lydia Y. Chen

Year: 2025 | Volume: 24, Issue: 4 | Journal Article |

HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data

Guozheng Li;Haotian Mi;Chi Harold Liu;Takayuki Itoh;Guoren Wang

IEEE Transactions on Visualization and Computer Graphics
Year: 2025 | Volume: 31, Issue: 1 | Journal Article |
When using exploratory visual analysis to examine multivariate hierarchical data, users often need to query data to narrow down the scope of analysis. However, formulating effective query expressions remains a challenge for multivariate hierarchical data, particularly when datasets become very large. To address this issue, we develop a declarative grammar, HiRegEx (Hierarchical data Regular Expres...Show More

HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data

Guozheng Li;Haotian Mi;Chi Harold Liu;Takayuki Itoh;Guoren Wang

Year: 2025 | Volume: 31, Issue: 1 | Journal Article |

MIBR: Bridging Domains through Diverse Interests for Cross-Domain Sequential Recommendation

Chengzhe Zhang;Xu Min;Changsheng Li;Xiaolu Zhang;Weichang Wu;Jun Zhou;Ye Yuan;Guoren Wang

2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Cross-Domain Sequential Recommendation (CDSR) aims to enhance personalized user experiences by leveraging user behaviors across multiple domains. Existing methods primarily focus on fusing information from various domains and modeling global user preferences, but often struggle with negative transfer, where knowledge from one domain impairs recommendation performance in another. For example, a use...Show More

MIBR: Bridging Domains through Diverse Interests for Cross-Domain Sequential Recommendation

Chengzhe Zhang;Xu Min;Changsheng Li;Xiaolu Zhang;Weichang Wu;Jun Zhou;Ye Yuan;Guoren Wang

Year: 2024 | Conference Paper |

FDNet: Feature Decoupling Framework for Trajectory Prediction

Yuhang Li;Changsheng Li;Baoyu Fan;Rongqing Li;Ziyue Zhang;Dongchun Ren;Ye Yuan;Guoren Wang

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Trajectory prediction plays a significant role in autonomous driving, with current challenges primarily focused on capturing complex interactions in traffic scenes. Previous methods usually directly encode non-interactive and interactive information together, and then decode them for trajectory prediction. However, given the complexity inherent property in the trajectory generation process (e.g., ...Show More

FDNet: Feature Decoupling Framework for Trajectory Prediction

Yuhang Li;Changsheng Li;Baoyu Fan;Rongqing Li;Ziyue Zhang;Dongchun Ren;Ye Yuan;Guoren Wang

Year: 2024 | Conference Paper |

An Intelligent Task and Data Deployment Method for SIoT Systems

Hangxu Ji;Yongjiao Sun;Yuyao Luan;Ye Yuan;Guoren Wang;Qi Wang

IEEE Internet of Things Journal
Year: 2024 | Early Access Article |
The SIoT system enables connectivity among smart devices by integrating social networks with the Internet of Things. This integration is essential for advancing intelligent services and applications, as well as enhancing the commercial value of data. Rational task and data deployment strategies allow different types of devices to perform optimally in their areas of expertise, reducing network load...Show More

An Intelligent Task and Data Deployment Method for SIoT Systems

Hangxu Ji;Yongjiao Sun;Yuyao Luan;Ye Yuan;Guoren Wang;Qi Wang

Year: 2024 | Early Access Article |

A Novel Text Adversarial Sample Generation and Defense Method for SIoT Systems

Hangxu Ji;Jiale Guo;Yongjiao Sun;Ye Yuan;Guoren Wang;Qi Wang

IEEE Internet of Things Journal
Year: 2024 | Early Access Article |
The generation and defense of text adversarial samples are crucial for improving the robustness and security of SIoT systems, as the exchange of information between devices in SIoT relies heavily on NLP technology. However, the discrete nature of text data leads to a lack of contextual integration in current mainstream adversarial sample generation methods based on text replacement. This results i...Show More

A Novel Text Adversarial Sample Generation and Defense Method for SIoT Systems

Hangxu Ji;Jiale Guo;Yongjiao Sun;Ye Yuan;Guoren Wang;Qi Wang

Year: 2024 | Early Access Article |

Many Hands Make Light Work: Group Influence Maximization in Evolving Social Networks

Yuliang Ma;Yu Chen;Peng Wei;Ye Yuan;Guoren Wang;Zhong-Zhong Jiang

IEEE Transactions on Big Data
Year: 2024 | Early Access Article |
As the adage ”many hands make light work” suggests, collaborative influence often surpasses individual influence. Inspired by this insight, we undertook a study on group influence maximization in evolving social networks, which is applicable to domains such as social media marketing and financial risk management. Our goal is to reveal how collaborative influence propagates in dynamic settings. Exi...Show More

Many Hands Make Light Work: Group Influence Maximization in Evolving Social Networks

Yuliang Ma;Yu Chen;Peng Wei;Ye Yuan;Guoren Wang;Zhong-Zhong Jiang

Year: 2024 | Early Access Article |

Spatiotemporal Learning With Decoupled Causal Attention for Multivariate Time Series

Xin Bi;Qinghan Jin;Meiling Song;Xin Yao;Xiangguo Zhao;Ye Yuan;Guoren Wang

IEEE Transactions on Big Data
Year: 2024 | Early Access Article |
In multivariate time series prediction tasks, the inter- and intra-variable relations have significant influence on prediction outcomes. In many engineering and industrial scenarios, the multivariate time series also contain a large number of subjective influencing factors such as settings and behaviors of users. Existing learning methods neglect the interactions of these subjective factors among ...Show More

Spatiotemporal Learning With Decoupled Causal Attention for Multivariate Time Series

Xin Bi;Qinghan Jin;Meiling Song;Xin Yao;Xiangguo Zhao;Ye Yuan;Guoren Wang

Year: 2024 | Early Access Article |

Where Shall We Go: Point-of-Interest Group Recommendation With User Preference Embedding

Yuliang Ma;Zhong-Zhong Jiang;Mingyang Sun;Ye Yuan;Guoren Wang

IEEE Transactions on Big Data
Year: 2024 | Early Access Article |
In the context of geo-social networks, the objective of Point-of-Interest (POI) group recommendation is to propose POIs that align with the preferences of all members within a specific temporal group. POI group recommendation is significant in enhancing user experience, promoting social interaction, and providing convenient access to information. It also aids in community building and business pro...Show More

Where Shall We Go: Point-of-Interest Group Recommendation With User Preference Embedding

Yuliang Ma;Zhong-Zhong Jiang;Mingyang Sun;Ye Yuan;Guoren Wang

Year: 2024 | Early Access Article |

DREAM: Domain-Agnostic Reverse Engineering Attributes of Black-Box Model

Rongqing Li;Jiaqi Yu;Changsheng Li;Wenhan Luo;Ye Yuan;Guoren Wang

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 12 | Journal Article |
Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a target black-box model can be exposed through a sequence of queries. There is a crucial limitation: these works assume the training dataset of the target model is known beforehand and leverage this dataset for model att...Show More

DREAM: Domain-Agnostic Reverse Engineering Attributes of Black-Box Model

Rongqing Li;Jiaqi Yu;Changsheng Li;Wenhan Luo;Ye Yuan;Guoren Wang

Year: 2024 | Volume: 36, Issue: 12 | Journal Article |

On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving

Kaituo Feng;Changsheng Li;Dongchun Ren;Ye Yuan;Guoren Wang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However, the oversized neu-ral networks render them impractical for deployment on resource-constrained systems, which unavoidably requires more computational time and resources during reference. To handle this, knowledge distillation offers a promising approach that...Show More

On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving

Kaituo Feng;Changsheng Li;Dongchun Ren;Ye Yuan;Guoren Wang

Year: 2024 | Conference Paper |

Learning to Generate Parameters of ConvNets for Unseen Image Data

Shiye Wang;Kaituo Feng;Changsheng Li;Ye Yuan;Guoren Wang

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e.g., SGD or Adam) to learn network parameters, making training very time- and resource-intensive. In this paper, we propose a new training paradigm and formulate the parameter learning of ConvNets into a prediction task: considering that there exist cor...Show More

Learning to Generate Parameters of ConvNets for Unseen Image Data

Shiye Wang;Kaituo Feng;Changsheng Li;Ye Yuan;Guoren Wang

Year: 2024 | Volume: 33 | Journal Article |

AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

Xunkai Li;Zhengyu Wu;Wentao Zhang;Henan Sun;Rong-Hua Li;Guoren Wang

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semisupervised node classification settings. Such a strategy assumes the consistency of topology betwe...Show More

AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

Xunkai Li;Zhengyu Wu;Wentao Zhang;Henan Sun;Rong-Hua Li;Guoren Wang

Year: 2024 | Conference Paper |

Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification

Henan Sun;Xunkai Li;Zhengyu Wu;Daohan Su;Rong-Hua Li;Guoren Wang

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Recently, graph neural networks (GNNs) have shown prominent performance in semi-supervised node classification by leveraging knowledge from the graph database. However, most existing GNNs follow the homophily assumption, where connected nodes are more likely to exhibit similar feature distributions and the same labels, and such an assumption has proven to be vulnerable in a growing number of pract...Show More

Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification

Henan Sun;Xunkai Li;Zhengyu Wu;Daohan Su;Rong-Hua Li;Guoren Wang

Year: 2024 | Conference Paper |

Feed: Towards Personalization-Effective Federated Learning

Pengpeng Qiao;Kangfei Zhao;Bei Bi;Zhiwei Zhang;Ye Yuan;Guoren Wang

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Federated learning (FL) has become an emerging paradigm via cooperative training models among distributed clients without leaking data privacy. The performance degradation of F1 on heterogeneous data has driven the development of personalized FL (PFL) solutions, where different models are built for individual clients. However, existing PFL approaches often have limited personalization in terms of ...Show More

Feed: Towards Personalization-Effective Federated Learning

Pengpeng Qiao;Kangfei Zhao;Bei Bi;Zhiwei Zhang;Ye Yuan;Guoren Wang

Year: 2024 | Conference Paper |

Self-Training GNN-based Community Search in Large Attributed Heterogeneous Information Networks

Yuan Li;Xiuxu Chen;Yuhai Zhao;Wen Shan;Zhengkui Wang;Guoli Yang;Guoren Wang

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Attributed Heterogeneous Information Networks (AHINs) amalgamate the advantages of attributed graphs (AGs) and heterogeneous information networks (HINs) to model intri-cate systems. Within this context, community search-aiming to identify the most probable community containing the queried ver-tex-has been extensively explored in AGs and HINs. However, existing methodologies fall short in simultane...Show More

Self-Training GNN-based Community Search in Large Attributed Heterogeneous Information Networks

Yuan Li;Xiuxu Chen;Yuhai Zhao;Wen Shan;Zhengkui Wang;Guoli Yang;Guoren Wang

Year: 2024 | Conference Paper |

Efficient Partial Order Based Transaction Processing for Permissioned Blockchains

Shuai Zhao;Zhiwei Zhang;Junkai Wang;Ye Yuan;Meihui Zhang;Guoren Wang;Jiang Xiao

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
With the development of permissioned blockchains, transaction processing plays an increasingly crucial role in improving performance. The execution and consensus phases in existing transaction processing methods are based on total order. The consensus phase constructs a total order representing the execution order and submission order of different transactions. Then, in the execution phase, transa...Show More

Efficient Partial Order Based Transaction Processing for Permissioned Blockchains

Shuai Zhao;Zhiwei Zhang;Junkai Wang;Ye Yuan;Meihui Zhang;Guoren Wang;Jiang Xiao

Year: 2024 | Conference Paper |

Maximal Biclique Enumeration: A Prefix Tree Based Approach

Jiujian Chen;Kai Wang;Rong-Hua Li;Hongchao Qin;Xuemin Lin;Guoren Wang

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Bipartite graphs are commonly used to model relationships between two distinct types of entities, such as customer-product relationships in e-commerce platforms and protein-protein interactions in bioinformatics. Enumerating all maximal bicliques from a bipartite graph is a fundamental graph mining problem that has been widely used in many real-world applications including community search and spa...Show More

Maximal Biclique Enumeration: A Prefix Tree Based Approach

Jiujian Chen;Kai Wang;Rong-Hua Li;Hongchao Qin;Xuemin Lin;Guoren Wang

Year: 2024 | Conference Paper |

Mining Quasi-Periodic Communities in Temporal Network

Yue Zeng;Hongchao Qin;Rong-Hua Li;Kai Wang;Guoren Wang;Xuemin Lin

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Periodic group behaviors often exist in temporal interaction networks, such as monthly group meetings, quarterly animal migrations, and yearly birthday parties. In real life, these events are usually quasi-periodic, meaning that the time intervals between two adjacent events are nearly constant but not exactly constant. Most existing studies mainly focus on identifying exact periodic group behavio...Show More

Mining Quasi-Periodic Communities in Temporal Network

Yue Zeng;Hongchao Qin;Rong-Hua Li;Kai Wang;Guoren Wang;Xuemin Lin

Year: 2024 | Conference Paper |

Mitigating Data Scarcity in Supervised Machine Learning Through Reinforcement Learning Guided Data Generation

Chengliang Chai;Kasisen Jin;Nan Tang;Ju Fan;Lianpeng Qiao;Yuping Wang;Yuyu Luo;Ye Yuan;Guoren Wang

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
One primary problem for supervised ML is data scarcity, which refers to the inadequacy of well-labeled training data. Recently, deep generative models have shown the capability of generating data objects that closely resemble real data for datasets in different modalities, including images, natural language, and tabular data. Naturally, a promising approach for tackling data scarcity involves trai...Show More

Mitigating Data Scarcity in Supervised Machine Learning Through Reinforcement Learning Guided Data Generation

Chengliang Chai;Kasisen Jin;Nan Tang;Ju Fan;Lianpeng Qiao;Yuping Wang;Yuyu Luo;Ye Yuan;Guoren Wang

Year: 2024 | Conference Paper |

Authenticated Keyword Search on Large-Scale Graphs in Hybrid-Storage Blockchains

Siyu Li;Zhiwei Zhang;Jiang Xiao;Meihui Zhang;Ye Yuan;Guoren Wang

2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
The widespread availability of Internet access and online services has led to the generation of numerous large-scale graphs in various real-world applications, such as online social networks and knowledge graphs. Keyword search stands out as a crucial task in the analysis and mining of these graphs. However, graph data owners tend to outsource storage and computation tasks to the cloud due to limi...Show More

Authenticated Keyword Search on Large-Scale Graphs in Hybrid-Storage Blockchains

Siyu Li;Zhiwei Zhang;Jiang Xiao;Meihui Zhang;Ye Yuan;Guoren Wang

Year: 2024 | Conference Paper |

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