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学者姓名：张勇
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Abstract ：
Pedestrian trajectory prediction is a key component for various applications that involve human and vehicle interactions, such as autonomous driving, traffic management and smart city planning. Existing methods based on graph neural networks have limited ability to capture group interactions and precisely model complex associations among multi-agents. To solve these problems, we propose OST-HGCN, an optimized hypergraph convolutional network. It models multi-agent trajectory interactions from both temporal and spatial perspectives using hypergraph structures, and optimizes the spatio-temporal hypergraph structure to enable fine-grained analysis of multi-agent trajectory motion intentions and high-order interactions. We employ OST-HGCN to a CVAE-based prediction framework, and use the optimized hypergraph structure to predict multi-agent plausible trajectories. We conduct extensive experiments on four real trajectory prediction datasets of NBA, NFL, SDD and ETH-UCY, and verify the effectiveness of the proposed OST-HGCN.
Keyword ：
Trajectory Trajectory Predictive models Predictive models Pedestrians Pedestrians Accuracy Accuracy Long short term memory Long short term memory Generative adversarial networks Generative adversarial networks Optimization Optimization hypergraph convolution network hypergraph convolution network hypergraph structure optimization hypergraph structure optimization Data models Data models multi-agent interaction modeling multi-agent interaction modeling Convolutional neural networks Convolutional neural networks Market research Market research Trajectory prediction Trajectory prediction
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| GB/T 7714 | Lin, Xuanqi , Zhang, Yong , Wang, Shun et al. OST-HGCN: Optimized Spatial-Temporal Hypergraph Convolution Network for Trajectory Prediction [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2025 , 26 (3) : 3056-3070 . |
| MLA | Lin, Xuanqi et al. "OST-HGCN: Optimized Spatial-Temporal Hypergraph Convolution Network for Trajectory Prediction" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 26 . 3 (2025) : 3056-3070 . |
| APA | Lin, Xuanqi , Zhang, Yong , Wang, Shun , Hu, Yongli , Yin, Baocai . OST-HGCN: Optimized Spatial-Temporal Hypergraph Convolution Network for Trajectory Prediction . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2025 , 26 (3) , 3056-3070 . |
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Abstract ：
Predicting whether crime events will occur indifferent areas (framed as a classification task) is atypical spatio-temporal data mining problem, crucial for both urban management and public safety. Contemporary crime occurrence prediction models predominantly leverage deep learning techniques, focusing on capturing the spatio-temporal dependencies within crime data. Analysis of crime data reveals correlations among different crime types, indicating shared change patterns. Leveraging these correlations among crime types significantly enhances the accuracy of crime occurrence predictions. Nevertheless, existing crime occurrence prediction models frequently overlook the utilization of these type correlations. To solve this problem, we propose a new crime occurrence prediction model with multi-type crime correlation learning: the Multi-type Relations Aware Graph Neural Networks (MRAGNN). The model dynamically constructs a spatial/type graph structure of crime data and employs dynamic graph networks to capture both spatio-temporal and type-temporal dependencies within the data. We introduce a cross-modal gated fusion mechanism to fuse the representations of two dependencies. Furthermore, we develop an improved multi-label classification focal loss to address the challenges posed by the imbalance in crime occurrence data on classification results. Experimental results validate that our model outperforms state-of-the-art (SOTA) methods in crime occurrence prediction.
Keyword ：
Crime type correlations modeling Crime type correlations modeling Graph neural network Graph neural network Crime occurrence prediction Crime occurrence prediction
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| GB/T 7714 | Wang, Shun , Zhang, Yong , Piao, Xinglin et al. MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with multi-type crime correlation learning [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 265 . |
| MLA | Wang, Shun et al. "MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with multi-type crime correlation learning" . | EXPERT SYSTEMS WITH APPLICATIONS 265 (2025) . |
| APA | Wang, Shun , Zhang, Yong , Piao, Xinglin , Lin, Xuanqi , Hu, Yongli , Yin, Baocai . MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with multi-type crime correlation learning . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 265 . |
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Abstract ：
Precipitation nowcasting within 2 h is an important and hard issue in the weather research area. Benefiting from the outstanding nonlinear relationship modeling capability, methods based on deep learning (DL) have achieved significant success in the task of precipitation nowcasting compared to the others. However, existing DL-based methods always disregard the intricate high-order correlations and lack substantial connections with the evolution of the precipitation system, which would lead to blurred forecasts and implausible predictions. To address these issues, we proposed a new Precipitation Nowcasting Network within a 2-h model based on the Hypergraph Neural Network (PN-HGNN). In this work, a Hypergraph Neural Network is first adopted for extracting spatiotemporal dynamic echo features. Second, regulation evolution is in charge of capturing the memory features to guide the extrapolation. Finally, we design a dual branch module to extrapolate the radar echoes. The proposed model has been assessed on the dataset HKO-7. The experimental results demonstrate that PN-HGNN achieved better prediction performance than the six representative echo extrapolation models.
Keyword ：
Extrapolation Extrapolation Predictive models Predictive models precipitation nowcasting precipitation nowcasting Task analysis Task analysis Neural networks Neural networks Meteorology Meteorology Radar Radar Hypergraph neural network (HGNN) Hypergraph neural network (HGNN) Precipitation Precipitation MotionRNN MotionRNN radar echo extrapolation radar echo extrapolation
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| GB/T 7714 | Sun, Xiaoni , Zhang, Yong , Piao, Xinglin et al. PN-HGNN: Precipitation Nowcasting Network Via Hypergraph Neural Networks [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Sun, Xiaoni et al. "PN-HGNN: Precipitation Nowcasting Network Via Hypergraph Neural Networks" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Sun, Xiaoni , Zhang, Yong , Piao, Xinglin , Wu, Jiayi , Jing, Guodong , Yin, Baocai . PN-HGNN: Precipitation Nowcasting Network Via Hypergraph Neural Networks . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Abstract ：
This research aims to investigate the intricate dynamics between energy futures prices and both tropical and traditional commodities futures prices, with a particular focus on the critical role of oil futures prices in predicting the trajectory of agricultural futures prices. The study employs the Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC-MGARCH) model to analyse the interplay among oil and agricultural futures price series over 2008:01-2021:06. Additionally, it incorporates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to highlight the differences in how energy futures prices predict tropical and traditional agricultural futures prices. The findings reveal numerous connections between oil prices and both tropical and traditional agricultural futures prices, underscoring the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights from this study provide valuable guidance for futures market participants, encouraging them to use these findings to refine and optimize their market strategies, thus enhancing their ability to navigate and capitalize on the complexities of these interconnected markets.
Keyword ：
Oil futures markets Oil futures markets STIR-HGNN model STIR-HGNN model DCC-MGARCH model DCC-MGARCH model agricultural futures prices forecast agricultural futures prices forecast
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| GB/T 7714 | Zhang, Wei , Wu, Jiayi , Wang, Shun et al. Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets [J]. | APPLIED ECONOMICS LETTERS , 2024 . |
| MLA | Zhang, Wei et al. "Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets" . | APPLIED ECONOMICS LETTERS (2024) . |
| APA | Zhang, Wei , Wu, Jiayi , Wang, Shun , Zhang, Yong . Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets . | APPLIED ECONOMICS LETTERS , 2024 . |
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Abstract ：
Recommender systems have become an indispensable tool in today's digital age, significantly enhancing user engagement on various online platforms by curating personalized item recommendations tailored to individual preferences. While the field has long been dominated by the collaborative filtering technique, which primarily leverages user-item interaction data, it often falls short in encapsulating the rich contextual intricacies and evolving dynamics inherent to these interactions. Recognizing this limitation, our research introduces the contextual semantic interaction graph embedding (CSI-GE) method. This advanced model incorporates a dynamic hop window within a multilayer graph convolutional network, ensuring a comprehensive extraction of both immediate and evolving contextual features. By amalgamating self-supervised contrastive learning, we achieve a refinement of user and item embeddings. Furthermore, our innovative variance-invariance-covariance (VIC) regularization-based loss function fortifies the robustness of these embeddings. Through rigorous testing, CSI-GE consistently outperformed contemporary methods, underscoring its superior accuracy and stability.
Keyword ：
recommender systems recommender systems hop window hop window Data models Data models Vectors Vectors Semantics Semantics Robustness Robustness graph neural networks (GNNs) graph neural networks (GNNs) Collaboration Collaboration Collaborative filtering Collaborative filtering Recommender systems Recommender systems Nonhomogeneous media Nonhomogeneous media self-supervised contrastive learning self-supervised contrastive learning
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| GB/T 7714 | Zhao, Shiyu , Zhang, Yong , Li, Mengran et al. Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 , 11 (5) : 6333-6346 . |
| MLA | Zhao, Shiyu et al. "Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 11 . 5 (2024) : 6333-6346 . |
| APA | Zhao, Shiyu , Zhang, Yong , Li, Mengran , Piao, Xinglin , Yin, Baocai . Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 , 11 (5) , 6333-6346 . |
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Abstract ：
Frontal person images contain the richest detailed features of humans, which can effectively assist in behavioral recognition, virtual dress fitting and other applications. While many remarkable networks are devoted to the person image generation task, most of them need accurate target poses as the network inputs. However, the target pose annotation is difficult and time-consuming. In this work, we proposed a first frontal person image generation network based on the proposed anchor pose set and the generative adversarial network. Specifically, our method first classify a rough frontal pose to the input human image based on the proposed anchor pose set, and regress all key points of the rough frontal pose to estimate an accurate frontal pose. Then, we consider the estimated frontal pose as the target pose, and construct a two-stream generator based on the generative adversarial network to update the person's shape and appearance feature in a crossing way and generate a realistic frontal person image. Experiments on the challenging CMU Panoptic dataset show that our method can generate realistic frontal images from arbitrary-view human images.
Keyword ：
arbitrary-view images arbitrary-view images frontal person image generation frontal person image generation frontal pose estimation frontal pose estimation deep learning deep learning generative adversarial networks generative adversarial networks
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| GB/T 7714 | Zhang, Yong , Zhang, Yuqing , Chen, Lufei et al. Frontal person image generation based on arbitrary-view human images [J]. | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (4) . |
| MLA | Zhang, Yong et al. "Frontal person image generation based on arbitrary-view human images" . | COMPUTER ANIMATION AND VIRTUAL WORLDS 35 . 4 (2024) . |
| APA | Zhang, Yong , Zhang, Yuqing , Chen, Lufei , Yin, Baocai , Sun, Yongliang . Frontal person image generation based on arbitrary-view human images . | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (4) . |
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Abstract ：
Trajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning-based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real-world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global-local scene-enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global-local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios. Comparison of scene information learning approaches. image
Keyword ：
trajectory prediction trajectory prediction scene-aware information integration scene-aware information integration multiagent systems multiagent systems
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| GB/T 7714 | Lin, Xuanqi , Zhang, Yong , Wang, Shun et al. Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network [J]. | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (3) . |
| MLA | Lin, Xuanqi et al. "Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network" . | COMPUTER ANIMATION AND VIRTUAL WORLDS 35 . 3 (2024) . |
| APA | Lin, Xuanqi , Zhang, Yong , Wang, Shun , Piao, Xinglin , Yin, Baocai . Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network . | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2024 , 35 (3) . |
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Abstract ：
Weather forecasting is inextricably linked to human lives and represents a quintessential task of spatiotemporal modeling, necessitated by the spatial and temporal dependencies inherent in meteorological data. Recent studies have consistently shown the excellent performance of graph-based neural networks in accurately modeling spatiotemporal data across various applications. Yet, traditional graph neural networks (GNNs) are unable to handle the high-order diffusion and aggregation phenomena between meteorological data caused by advection. Moreover, the impacts of spatial correlation among multisource information and the presence of noise in meteorological data are often overlooked. This study proposes a novel approach for modeling the spatiotemporal dependencies in meteorological data using the multi-information spatiotemporal aggregation and estrangement hypergraph convolution network. This method employs a novel representation of meteorological data using hypergraphs to address the aforementioned challenges. Specifically, we construct adjacency and semantic hypergraphs to represent spatial correlations and then introduce aggregation and estrangement hypergraph convolution networks to effectively capture multi-information spatial correlations. A new reconstruction feature attention module has been developed to fuse aggregation and estrangement semantic spatial information across various subspaces. In addition, the hypergraph convolution is embedded within a recurrent neural network architecture to model the temporal correlations. Extensive experiments have been conducted on four weather datasets, and state-of-the-art performance has been achieved in comparison to mainstream baseline methods.
Keyword ：
Predictive models Predictive models Noise Noise Correlation Correlation hypergraph hypergraph Atmospheric modeling Atmospheric modeling Estrangement Estrangement graph neural network (GNN) graph neural network (GNN) Meteorology Meteorology Convolution Convolution Weather forecasting Weather forecasting Graph neural networks Graph neural networks weather forecasting weather forecasting Accuracy Accuracy Data models Data models
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| GB/T 7714 | Miao, Zhuangzhuang , Zhang, Yong , Wu, Jiayi et al. Multi-Information Aggregation and Estrangement HyperGraph Convolutional Networks for Spatiotemporal Weather Forecasting [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Miao, Zhuangzhuang et al. "Multi-Information Aggregation and Estrangement HyperGraph Convolutional Networks for Spatiotemporal Weather Forecasting" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Miao, Zhuangzhuang , Zhang, Yong , Wu, Jiayi , Jing, Guodong , Piao, Xinglin , Yin, Baocai . Multi-Information Aggregation and Estrangement HyperGraph Convolutional Networks for Spatiotemporal Weather Forecasting . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Abstract ：
The transportation system is a complex dynamic giant system which integrates and intertwines the elements of people, vehicles, roads, and the environment. The city-level traffic flow forecasting can effectively reflect the flow changes of the traffic system and provide practical guidance for the formulation of traffic rules. Recent city-level traffic flow forecasting works rely on accurate prior knowledge of graphs (i.e., the spatial relationships between roads), which hinders their effectiveness and application in the real world. We propose a novel framework for urban traffic flow forecasting, which simultaneously infers and utilizes the relationship between time series. In our model, the graph structure learning module dynamically captures the correlation and causation between the different time series and infers a potentially fully connected graph. At the same time, the temporal convolution network captures the temporal correlation between a single time series. The graph neural network uses the graph for forecasting. Our model no longer relies on accurate graph priors and achieves better forecasting results than previous work. Experiments on two public datasets verify that the proposed model is very competitive.
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| GB/T 7714 | Huo, Guangyu , Zhang, Yong , Lv, Yimei et al. Urban Traffic Flow Forecasting Based on Graph Structure Learning [J]. | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 (1) . |
| MLA | Huo, Guangyu et al. "Urban Traffic Flow Forecasting Based on Graph Structure Learning" . | JOURNAL OF ADVANCED TRANSPORTATION 2024 . 1 (2024) . |
| APA | Huo, Guangyu , Zhang, Yong , Lv, Yimei , Ren, Hao , Yin, Baocai . Urban Traffic Flow Forecasting Based on Graph Structure Learning . | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 (1) . |
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Abstract ：
Missing data in time series is a pervasive problem that serves as obstacles for subsequent traffic data analysis. Consequently, extensive research works have been conducted on traffic missing data imputation tasks. The state-of-the-art traffic data imputation models are mostly based on recurrent neural networks. However, these methods belong to autoregressive models which are highly susceptible to error propagation. The attention-based methods are non-autoregressive models that can avoid compounding errors and help achieve better imputation quality. Moreover, the attention-based methods in now widely applied and have achieved remarkable results, whereas their application on traffic data imputation is still limited. Thus, this paper proposes Self-Attention Graph Convolution Imputation Network (SAGCIN) for spatio-temporal traffic data. To ensure the accuracy of data imputation, it is necessary to fully capture the spatio-temporal contextual information of traffic data to impute missing values. To this end, the SAGCIN model incorporates self-attention mechanism with diffusion graph convolution network. The SAGCIN model consists of two spatio-temporal blocks with a spatio-temporal encoder and an imputation decoder. The encoder learns spatio-temporal representations specialized for traffic data imputation tasks. Based on the learned representation, the decoder performs two stages of imputation operator for missing data. A joint-optimization training approach of imputation and reconstruction is introduced for SAGCIN to perform missing value imputation for traffic data. Empirical results demonstrate that SAGCIN model outperforms state-of-the-art methods in imputation tasks on relevant real-world benchmarks.
Keyword ：
diagonally-masked self-attention diagonally-masked self-attention missing values missing values diffusion graph convolution diffusion graph convolution imputation model imputation model Traffic data Traffic data
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| GB/T 7714 | Wei, Xiulan , Zhang, Yong , Wang, Shaofan et al. Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
| MLA | Wei, Xiulan et al. "Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024) . |
| APA | Wei, Xiulan , Zhang, Yong , Wang, Shaofan , Zhao, Xia , Hu, Yongli , Yin, Baocai . Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
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