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dynamic graph-based bilateral recurrent imputation network for multivariate time series

NEURAL NETWORKS 2025年 186卷 107298页

作者： Lai, Xiaochen Zhang, Zheng Zhang, Liyong Lu, Wei Li, Zhuohan Dalian Univ Technol Sch Software Dalian 116600 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

Multivariate time series imputation using graph neural networks (GNNs) has gained significant attention, where the variables and their correlations are depicted as the graph nodes and edges, offering a structured way to understand the intricacies of multivariate time series. On this basis, existing GNNs typically make the assumption of static correlations between variables, using a graph with fixed edge weights to model multivariate relationships. However, the static assumption is usually inconsistent with the dynamic nature of real-world data, where correlations between variables tend to change over time. In this paper, we propose a dynamic graph-based bilateral recurrent imputation network (DGBRIN) to address the above issue. Specifically, for each segment of a multivariate time series captured within a sliding window, we construct a specialized graph to capture the localized, dynamic correlations between variables. To this end, we design a dynamic adjacency matrix learning (DAML) module, which integrates temporal dependencies through an information fusion layer and mine localized monotonic correlations between variables using the Spearman rank correlation coefficient. These correlations are represented in segment-specific adjacency matrices. Subsequently, the adjacency matrices and time series are fed into a hybrid graph-based bilateral recurrent network for missing value imputation, which combines the advantages of recurrent neural networks and graph convolutional networks to effectively capture temporal dependencies and merge the correlation information between variables. We conduct experiments on eight real-world time series. The results demonstrate the effectiveness of the proposed model.

关键词： Multivariate time series Missing value imputation dynamic graph Recurrent neural network graph convolutional network

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dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval

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MEDICAL IMAGE ANALYSIS 2025年 101卷 103468页

作者： Jin, Haochen Shen, Junyi Cui, Lei Shi, Xiaoshuang Li, Kang Zhu, Xiaofeng Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Sichuan Univ West China Hosp Dept Gen Surg Div Liver Surg Chengdu 610044 Peoples R China Northwest Univ China Dept Comp Sci & Technol Xian 710075 Peoples R China Sichuan Univ West China Med Ctr Chengdu 610041 Peoples R China

Recently, a multi-scale representation attention based deep multiple instance learning method has proposed to directly extract patch-level image features from gigapixel whole slide images (WSIs), and achieved promising performance on multiple popular WSI datasets. However, it still has two major limitations: (i) without considering the relations among patches, thereby possibly restricting the model performance;(ii) unable to handle retrieval tasks, which is very important in clinic diagnosis. To overcome these limitations, in this paper, we propose a novel end-to-end MIL-based deep hashing framework, which is composed of a multi-scale representation attention based deep network as the backbone, patch-based dynamic graphs and hashing encoding layers, to simultaneously handle classification and retrieval tasks. Specifically, the multi- scale representation attention based deep network is to directly extract patch-level features from WSIs with mining the significant information at cell-, patch- and bag-level features. Additionally, we design a novel patch- based dynamic graph construction method to learn the relations among patches within each bag. Moreover, the hashing encoding layers are to encode patch- and WSI-level features into binary codes for patch- and WSI-level image retrieval. Extensive experiments on multiple popular datasets demonstrate that the proposed framework outperforms recent state-of-the-art ones on both classification and retrieval tasks. All source codes are available at https://***/hcjin0816/DG_WSDH.

关键词： Whole slide images Attention-based MIL Hashing encoding dynamic graph

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dynamic graph Convolutional Recurrent Network With Spatiotemporal Category Information Embedding for Traffic Flow Prediction

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IEEE INTERNET OF THINGS JOURNAL 2024年第24期11卷 39473-39486页

作者： Zhu, Guodong Niu, Yunyun Du, Songzhi Wang, Pengcheng China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China

Traffic flow prediction is a challenging spatiotemporal prediction task due to its spatiotemporal dynamics and uncertainty. In recent years, graph convolutional neural networks (GCNs) have been applied to traffic flow prediction due to their exceptional capabilities in processing network information. However, current GCN-based methods fail to capture long-range spatial dependencies between regions with similar functionalities, and the modeling of spatial dependencies within adjacent time steps is neglected. To solve these problems, this work proposes a new dynamic graph convolutional recurrent network (CDGCRN) with the spatiotemporal category information embedding. An embedding layer with spatiotemporal category information is introduced to enhance the model's capability in capturing long-range spatial dependencies. This layer aids the model in rapidly identifying traffic patterns with distinct features. Morever, dynamic spatial information fusion graph convolutional recurrent unit is proposed to capture spatial dependencies within all time steps and adjacent time steps. It incorporates a learnable graph structure with a time window for capturing spatial dependency relationships within adjacent time steps and a globally learnable graph structure for learning spatial dependency relationships across all time steps. Experimental results on the four real-world data sets demonstrate that CDGCRN is not only computationally efficient but also outperforms 17 state-of-the-art baseline methods.

关键词： Spatiotemporal phenomena Predictive models Data models Adaptation models Convolution Computational modeling Forecasting dynamic graph spatial-temporal systems spatiotemporal category information embedding traffic flow forecasting

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dynamic graph Representation Learning via Coupling-Process Model

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第9期35卷 12383-12395页

作者： Duan, Pingtao Zhou, Chuan Liu, Yuting Beijing Jiaotong Univ Sch Math & Stat Beijing 100044 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

Representation learning based on dynamic graphs has received a lot of attention in recent years due to its wide range of application scenarios. Although many discrete or continuous dynamic graph representation learning methods have been proposed, many of them ignore the role of edge types. Through the observation of dynamic graphs in the real world, it is found that the types of various edges are very different in nature. They are roughly divided into two categories according to the frequency of occurrence: evolutive edges that appear infrequently and interactive edges that appear frequently. For both types of edges, we propose a coupling-process model (DyCPM) to capture the dynamic mechanisms of them. The model not only generates low-dimensional embedding vectors of nodes, but also aggregates the structural information and temporal information of two kinds of edges. In particular, we design a neural network parameterized discrete process to depict the change law of the topology of evolutive edges and a neural network parameterized temporal point process (TPP) to characterize the temporal dynamic rule of interactive edges. More importantly, we propose a coupling mechanism to transfer the information of the two processes through a shared embedding matrix and finally generate an embedding matrix that aggregates the topology information and temporal information of the two kinds of edges for the dynamic link prediction task. We evaluate our model and several baselines on real datasets. The experimental results show that our model can better aggregate the topology information and temporal information of the two kinds of edges according to their properties and outperforms several state-of-the-art baselines in the performance of dynamic link prediction.

关键词： Representation learning Topology Social networking (online) Aggregates Task analysis Recurrent neural networks Network topology dynamic graph dynamic link prediction representation learning temporal point process (TPP)

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dynamic graph Temporal-Frequency Dual-Channel Network for Multi-Band Spectrum Prediction

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IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2940-2944页

作者： Guo, Xing Xu, Yitao Sun, Jiachen Ding, Guoru Lin, Fandi Song, Yehui Army Engn Univ Coll Commun Engn Nanjing 210007 Peoples R China

Spectrum prediction is crucial in cognitive radio networks, and previous studies have introduced various spectrum modeling methods. However, these methods typically only capture static patterns from historical spectrum data, overlooking the dynamic nature of the spectrum environment. Therefore, this letter proposes a dynamic graph temporal-frequency dual-channel network (DGTFDN) to capture the time-varying correlations among multiple frequency bands and adaptively update and aggregate features. Additionally, the proposed method employs a dual-channel network to simultaneously model both non-structural and structural features from multiple frequency bands, and then utilizes an adaptive gating mechanism to fuse the two types of features. The experimental results show that the proposed method achieves better prediction results than other comparative methods, with improvements of 0.3638, 0.7828, and 2.7077% in MAE, MRSE, and MAPE respectively.

关键词： Feature extraction Time-frequency analysis Predictive models Long short term memory Adaptation models Windows Correlation Spectrum prediction dynamic graph convolutional neural network long short-term memory

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Recognition of carrier-based aircraft flight deck operations based on dynamic graph

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Chinese Journal of Aeronautics 2025年第3期38卷 474-490页

作者： Xingyu GUO Jiaxin LI Hua WANG Junnan LIU Yafei LI Mingliang XU School of Computer Science and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Engineering Research Center of Intelligent Swarm Systems Ministry of EducationZhengzhou 450001China National Supercomputing Center in Zhengzhou Zhengzhou 450001China School of Geo-Science&Technology Zhengzhou UniversityZhengzhou 450001China

Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.

关键词： Carrier-based aircraft Flight deck operation Operation recognition Long spatial-temporal trajectories dynamic graph Biased random walk graph embeddings

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dynamic graph-Based Adaptive Learning for Online Industrial Soft Sensor With Mutable Spatial Coupling Relations

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第9期70卷 9614-9622页

作者： Zhu, Kun Zhao, Chunhui Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ NGIC Platform Hangzhou 310027 Peoples R China

The accurate online soft sensor in complex industrial processes remains challenging because underlying spatial coupling relations among process variables have not been effectively mined and exploited. Recently, some deep learning based studies construct static graphs to explicitly represent underlying spatial coupling relations among process variables, but they neglect the fact that spatial coupling relations have mutable characteristics, leading to poor performance in the online soft sensor. Therefore, in this article, we propose a novel deep learning model to address this issue to achieve accurate soft sensors. Specifically, a dynamic graph is proposed to realize adaptive learning and automatic inference for mutable spatial coupling relations so that the proposed model is endowed with the ability to real-timely sense spatial coupling relations in the online industrial soft sensor. Then, based on the dynamic graph, a new multihop attention graph convolutional network is proposed to systematically aggregate various crucial node feature representations during graph convolution processes to capture fine-grained spatial dependence features, thereby achieving effective modeling for variation patterns of process variables. Finally, a new multivariate incremental training algorithm is designed for deep learning models to further improve the prediction performance. The verification study on a coal mill rig demonstrates the feasibility and effectiveness of the proposed model.

关键词： Couplings Soft sensors Deep learning Data models Training Convolution Feature extraction Adaptive learning dynamic graph multihop attention graph convolutional network (MAGCN) mutable spatial coupling relations online industrial soft sensor

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dynamic graph Embedding via Self-Attention in the Lorentz Space 27

Dynamic Graph Embedding via Self-Attention in the Lorentz Sp...

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

作者： Duan, Dingyang Zha, Daren Lie, Zeyi Chen, Yu Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Beijing Digital Educ Ctr Beijing Peoples R China

ISBN: (纸本)9798350349184;9798350349191

graph Neural Networks (GNNs) are popular for learning node representations in complex graph structures. Traditional methods use Euclidean space but struggle to capture hierarchical structures in real-world graphs. Besides, it's important to note that in practical applications, many graphs are dynamic and undergo continuous evolution over time. To investigate the characteristics of complex temporal networks, we have introduced a dynamic graph embedding model in the Lorentz space, building upon the foundation of the previously proposed DynHAT model. More specially, our model divides the dynamic graph into multiple discrete static graphs, maps each static graph to the Lorentz space, and then learns informative node representations over time using a self-attention mechanism. We have conducted link prediction experiments on two types of graphs: communication networks and rating networks. Through comprehensive experiments conducted on five real-world datasets, we have demonstrated the superiority of our model in embedding dynamic graphs within Lorentz space.

关键词： lorentz dynamic graph self-attention

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Performance optimization of heterogeneous computing for large-scale dynamic graph data

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-26页

作者： Wang, Haifeng Guo, Wenkang Zhang, Ming Linyi Univ Sch Informat Sci & Engn Linyi 276002 Shandong Peoples R China Linyi Univ Res Inst Shandong Prov Network Key Lab Linyi 276002 Shandong Peoples R China

The performance of machines has not been fully exploited when processing large-scale dynamic graph in heterogeneous GPU clusters. To improve the performance of graph computing, a distributed heterogeneous engine (DHE) has been designed. A new heterogeneous graph partitioning algorithm is implemented, which can achieve load balancing among internal nodes in GPU clusters. DHE introduces synergy model to quantify the co-computing performance of heterogeneous processors and designs computing pipelines to optimize the performance of accessing memory. The graph algorithms PageRank, CC, SSSP and K-core are selected for the experiment. Under the same conditions, DHE's partitioning algorithm improves scalability and load balancing ability compared to other graph partitioning algorithms. The heterogeneous computing pipeline exhibits better memory access compared to the basic engine. The experiments show that the synergy of this system converges to 1 stably. Compared with the other three graph partitioning algorithms, the system reduced processing time by approximately 20-30% and the performance is improved by a factor of 1.2 times.

关键词： Heterogeneous computing Distributed heterogeneous graph partitioning Pipelines dynamic graph

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Gravel Particle Shape Classification from Half-Particle Point Clouds using a dynamic graph Edge Convolution Neural Network

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COMPUTERS AND GEOTECHNICS 2025年 179卷

作者： Xi, Junbo Gao, Lin Zheng, Junxing Wang, Dong Wang, Gezhou Guan, Zhenchang Zheng, Jiajia Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China Minist Water Resources Changjiang River Sci Res Inst Key Lab Geotech Mech & Engn Wuhan 430010 Peoples R China Fuzhou Univ Coll Civil Engn Fuzhou 350108 Fujian Peoples R China China Rd & Bridge Corp Beijing 10001 Peoples R China

Obtaining the three-dimensional (3D) shape of gravel particles is essential for calculating their roundness and sphericity. However, cost-effective, and rapid non-penetrating 3D imaging technologies, such as 3D laser scanners, stereophotography, and structured light techniques, only capture the geometric shape of the upper half particles (2.5D particles), unable to penetrate a particle to acquire the shape of the lower half. Current algorithms cannot accurately classify real 3D particles using easily available and low-cost 2.5D particles. To address this issue, this study aims to develop a dynamic graph edge convolution neural network (DGECNN) based on deep learning, utilizing 2.5D point clouds to characterize and classify the roundness and sphericity of 3D particles. The dataset comprises 4200 2.5D point clouds labeled into 12 roundness-sphericity categories based on corresponding complete 3D particle characterizations. Experimental results demonstrate that with a sampling point of 1200 and a batch size of 64, the training time is relatively shorter, and the automatic classification accuracy reaches a peak of 90.76%. Finally, compared to the traditional 3D CG method, the DGECNN classification is equally applicable to sand-size particles and exhibits significant advantages in roundness-sphericity, volume, surface area, and convex hull volume.

关键词： Deep learning dynamic graph Edge convolution Particle shape classification Stereophotography

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