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TopoMSG:A Topology-Aware Multi-Scale Graph Network for Social Bot Detection 认领 引用
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作者 Junhui Xu Qi Wang +1 位作者 Chichen Lin Weijian Fan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1164-1178,共15页
Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the be... Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots:a higher number of connected components weakens their collaborative capabilities,thereby reducing their proportion within the overall network.However,current social bot detection methods still make limited use of topological features.Furthermore,both graph neural network(GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations,and existing studies lack an effective fusion of multi-scale information.To address these issues,this paper proposes a topology-aware multi-scale social bot detection method,which jointly learns local and global representations through a co-training mechanism.At the local level,topological features are effectively embedded into node representations,enhancing expressiveness while alleviating the over-smoothing problem in GNNs.At the global level,a clustering attention mechanism is introduced to learn global node representations,mitigating the over-globalization problem.Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches.Our code is publicly available at http://gffzze280b34df20547e2s99qfw5xo0wxx6w5n.ffgz.tsg.suse.edu.cn/TopoMSG-2C41/(accessed on 27 October 2025). 展开更多
关键词 Social bot detection graph neural network topological data analysis
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CoLM2S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information 认领 引用 被引量：2
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作者 Beibei Han Yingmei Wei +1 位作者 Qingyong Wang Shanshan Wan 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第4期1464-1479,共16页
Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of t... Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM2S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM2S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines. 展开更多
关键词 attributed multiplex graph network contrastive self‐supervised learning graph representation learning multiscale information
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Multi-Head Attention Graph Network for Few Shot Learning 认领 引用 被引量：6
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作者 Baiyan Zhang Hefei Ling +5 位作者 Ping Li Qian Wang Yuxuan Shi Lei Wu Runsheng Wang Jialie Shen 《Computers, Materials & Continua》 SCIE EI 2021年第8期1505-1517,共13页
The majority of existing graph-network-based few-shot models focus on a node-similarity update mode.The lack of adequate information intensies the risk of overtraining.In this paper,we propose a novel Multihead Attent... The majority of existing graph-network-based few-shot models focus on a node-similarity update mode.The lack of adequate information intensies the risk of overtraining.In this paper,we propose a novel Multihead Attention Graph Network to excavate discriminative relation and fulll effective information propagation.For edge update,the node-level attention is used to evaluate the similarities between the two nodes and the distributionlevel attention extracts more in-deep global relation.The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature.For node update,we embrace the label-level attention to soften the noise of irrelevant nodes and optimize the update direction.Our proposed model is veried through extensive experiments on two few-shot benchmark MiniImageNet and CIFAR-FS dataset.The results suggest that our method has a strong capability of noise immunity and quick convergence.The classication accuracy outperforms most state-of-the-art approaches. 展开更多
关键词 Few shot learning attention graph network
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Spatial distribution order parameter prediction of collective system using graph network 认领 引用
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作者 赵慧敏 王瑞 +1 位作者 赵偲 郑文 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第5期566-572,共7页
In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a n... In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network(MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions. 展开更多
关键词 order parameter graph network collective system active matter
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Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis 认领 引用 被引量：1
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作者 Chaosheng Tang Wenle Xu +3 位作者 Junding Sun Shuihua Wang Yudong Zhang Juan Manuel Górriz 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第6期3179-3200,共22页
Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous ap... Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global features.However,these approaches typically neglect the topological structure information of the graph during the global feature extraction stage.This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image classification.Our approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual images.Recognizing that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global features.Additionally,we introduce a G-LFF module to fuse the local and global features effectively. 展开更多
关键词 Convolutional neural networks Graph convolutional networks Graph pooling COVID-19
Task-adaptation graph network for few-shot learning 认领 引用
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作者 ZHAO Wencang LI Ming QIN Wenqian 《High Technology Letters》 EI CAS 2022年第2期164-171,共8页
Numerous meta-learning methods focus on the few-shot learning issue,yet most of them assume that various tasks have a shared embedding space,so the generalization ability of the trained model is limited.In order to so... Numerous meta-learning methods focus on the few-shot learning issue,yet most of them assume that various tasks have a shared embedding space,so the generalization ability of the trained model is limited.In order to solve the aforementioned problem,a task-adaptive meta-learning method based on graph neural network(TAGN) is proposed in this paper,where the characterization ability of the original feature extraction network is ameliorated and the classification accuracy is remarkably improved.Firstly,a task-adaptation module based on the self-attention mechanism is employed,where the generalization ability of the model is enhanced on the new task.Secondly,images are classified in non-Euclidean domain,where the disadvantages of poor adaptability of the traditional distance function are overcome.A large number of experiments are conducted and the results show that the proposed methodology has a better performance than traditional task-independent classification methods on two real-word datasets. 展开更多
关键词 meta-learning image classification graph neural network(GNN) few-shot learning
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Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting 认领 引用 被引量：1
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作者 Xiaojun Pu Jiaqi Zhu +3 位作者 Yunkun Wu Chang Leng Zitong Bo Hongan Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第3期769-786,共18页
Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning mode... Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting. 展开更多
关键词 adaptive COVID-19 forecasting dynamic intervention spatio-temporal graph neural networks
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Dynamic Graph Multi-Scale Network for Breast Cancer Classification Using eXplainable Artificial Intelligence with Class Imbalance Mitigation in Medical and Healthcare Systems 认领 引用
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作者 Tanzila Saba Muhammad Mujahid +1 位作者 Faten S.Alamri Roaa Khalil Mohamed Ali Abed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1239-1258,共20页
In the era of artificial intelligence,pattern recognition techniques have become fundamental in advancing medical image processing,diagnosis,and automated disease classification systems.Among various clinical challeng... In the era of artificial intelligence,pattern recognition techniques have become fundamental in advancing medical image processing,diagnosis,and automated disease classification systems.Among various clinical challenges,breast cancer is the second most dangerous leading cause of death in women worldwide.Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates.This study proposes a dynamic graph multi-scale network for breast cancer diagnosis,integrated with multi-scale convolutional feature extraction,a squeeze-and-excitation block,and a graph convolutional network to jointly model local spatial features and global contextual dependencies.To mitigate the limitations of the dataset,this work incorporated mammography-based augmentation techniques to enhance the datasets and also a synthetic minority oversampling technique to generate samples to balance the class and enhance model generalization.Several experiments are performed using large MIAS,INbreast,and DDSM mammogram datasets with an RTX-3080 GPU with hold-out split and cross-validation methods.Experimental results demonstrate that the proposed model achieves a 3.99%improvement compared to pretrained models,indicating its effectiveness in handling complex mammographic patterns.The approach achieves(0.9866–0.9943)accuracy with a confidence interval of 0.95 and 0.9882±0.0048 mean precision.The results demonstrate that the proposed approach significantly outperforms pretrained and existing models in terms of key performance metrics.Additionally,Grad-CAM is used to provide visual explanations,highlighting clinically relevant regions.The work demonstrates that the proposed approach performed more effectively in disease detection,offering transparent decision-making support,and enhance imaging-based screening techniques. 展开更多
关键词 Breast cancer deep learning medical image analysis graph network diagnosis healthcare systems
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Personalized Differential Privacy Graph Neural Network 认领 引用
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作者 Yanli Yuan Dian Lei +3 位作者 Chuan Zhang Zehui Xiong Chunhai Li Liehuang Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期498-500,共3页
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g... Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs). 展开更多
关键词 graph neural networks gnns personalized differential privacy graph learning privacy preservation data utility preserving privacy graph neural network
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Modularized Graph Convolutional Network 认领 引用
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作者 Tiantian He Zhixuan Duan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期737-739,共3页
Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighb... Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. 展开更多
关键词 graph convolution networks gcns capturing diverse relationships nodes representation learning modularized graph convolution network neighbor aggregation graph neural network modularized graph convolution network mgcn graph convolutional network
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A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification 认领 引用
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作者 Yuquan Gan Siyu Wu +3 位作者 Chang Su Nan Xiang Zhijie Xu Yushan Pan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期464-482,共19页
Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and com... Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and computational costs from calculating correlations between all tokens(especially as image size or spectral bands increase)and limited ability to model local boundary information due to lacking explicit enhancement mechanisms.This paper proposes a novel method,bridge transformer network fused with deep graph convolution(BTDGC),to address these issues.The framework includes three components as follows:a double random masking mechanism(DRMM)that forces the model to infer masked features from context during training,a bridge transformer(BT)module with bridge tokens for cross-region feature interaction and a Deep Graph Convolutional Pooling(DGCP)module that preserves spatial topology while aggregating hierarchical information.Experiments on standard hyperspectral datasets show BTDGC outperforms mainstream methods in classification accuracy and robustness,effectively balancing global modelling and local boundary representation.The code is available at http://gffzz188fe103f8f1460as99qfw5xo0wxx6w5n.ffgz.tsg.suse.edu.cn/jenny3489/BTDGC. 展开更多
关键词 convolution graph convolutional network masking mechanism transforms
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FedGNN:Federated Graph Neural Networks for Privacy-Preserving Cyber-Resilient Energy Optimization in IoT-Based Smart Grids 认领 引用
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作者 Alanoud Al Mazroa Fahad Masood +3 位作者 Bakri Hussain Awaji Mohammad Alhefdi Abeer Aljohani Jawad Ahmad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1462-1483,共22页
The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and priv... The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and privacy risks,as increased connectivity and data exchange can significantly disrupt energy management and system stability.Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks.This study proposes a privacy-preserving and cyber-resilient energy-optimization framework,FedGNN,for IoT-enabled smart grids that jointly integrates federated learning,graph neural network-based trust inference,and trust-aware energy dispatch.The framework dynamically learns node-level trust scores from multifeature measurements,including load,voltage,frequency,renewable generation,and battery storage,and incorporates them into real-time energy optimization.Results demonstrate that the proposed approach improves system resilience up to 12%,mitigates the impact of compromised nodes,and maintains operational reliability,while preserving the privacy of distributed data.A comparative analysis with baseline methods shows the proposed framework's superior performance in energy deviation,resilience,and trust-aware decision-making.The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids. 展开更多
关键词 Cyber security energy optimization graph neural networks IoT smart grids
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Few-shot learning-based rapid spatiotemporal wavefront prediction with a lightweight Fourier graph neural network in adaptive optics 认领 引用
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作者 JU TANG JIAYONG CHEN +5 位作者 ZHIJIAN QIN HAOCHENG FENG JIAZHEN DOU ZHENBO REN JIANGLEI DI YUWEN QIN 《Photonics Research》 SCIE EI CAS CSCD 2026年第7期2862-2875,共14页
Adaptive optics(AO)systems hold significant application values,but are fundamentally limited by the inherent time delay challenge between wavefront sensing and correction.Prediction algorithms have emerged to infer fu... Adaptive optics(AO)systems hold significant application values,but are fundamentally limited by the inherent time delay challenge between wavefront sensing and correction.Prediction algorithms have emerged to infer future wavefront evolution,thereby enabling predictive AO.However,existing algorithms often face tradeoffs among accuracy,data efficiency,generalization capacity,and response bandwidth.To address these limitations,here we propose,to our knowledge,a novel few-shot learning framework for spatiotemporal wavefront prediction,termed the Fourier graph neural network(FGNN).FGNN uniquely enables accurate forecasting of wavefront evolution using only few-shot wavefront frames.It also exhibits strong robustness under varying turbulence conditions,including changes in wind speed,Fried parameter,and wavefront sensor frequency.Among all comparative algorithms,FGNN possesses the lowest parameter count,the minimum computational complexity,and the shortest response time of 0.121 ms.It reduces the residual wavefront error by up to 70%and improves the peak intensity of the focused spot by∼30%in an experimental open-loop AO setup.By providing a data-efficient and computationally lightweight solution,FGNN serves as a powerful tool for advanced AO technology in specific scenarios,with significant potential for applications in astronomical observation and laser communications. 展开更多
关键词 computational complexity adaptive optics predictive aohoweverexisting infer future wavefront Fourier graph neural network spatiotemporal wavefront prediction predictive AO turbulence conditions
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KIG:A Knowledge Graph-Guided Iterative-Updating Graph Neural Network for Multisensor Time Series Time-Delay Estimation 认领 引用
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作者 Siyuan Xu Dong Pan +3 位作者 Zhaohui Jiang Zhiwen Chen Haoyang Yu Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期327-345,共19页
Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider... Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS,and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives,leading to unsatisfactory performances in follow-up applications.To address these challenges,this paper proposes a novel knowledge graph(KG)-guided iterative-updating graph neural network(GNN)for time-delay estimation(TDE)in MTS.Initially,a domain-specific KG is constructed from domain mechanism knowledge,providing a foundation for GNN's initialization.Next,capitalizing on the inherent structure of the graph topology,a GNN-based TDE method is developed.Then,a customized loss function is constructed,which synthesizes both the performances of downstream tasks and graph-based constraints.Moreover,an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further.Finally,experimental results across various regression and classification tasks on numerical simulation,public datasets,and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS. 展开更多
关键词 Blast furnace ironmaking process graph neural network(GNN) knowledge graph(KG) multisensor time series(MTS) temporal alignment time-delay estimation(TDE)
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Distribution Network Partitioning for Voltage Regulation Using Heterogeneous Graph Neural Networks Considering Cyber-Attacks Risk 认领 引用
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作者 Lei Xu Bo Zhang +1 位作者 Chunxia Dou Dong Yue 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期995-997,共3页
Dear Editor,The integration of distributed energy resources(DERs)and communication infrastructures makes distribution networks increasingly cyber-physical,requiring resilient and real-time voltage regulation.Network p... Dear Editor,The integration of distributed energy resources(DERs)and communication infrastructures makes distribution networks increasingly cyber-physical,requiring resilient and real-time voltage regulation.Network partitioning enables scalable control,yet existing methods often ignore communication and security constraints or rely on costly optimization,limiting practicality under dynamic and adversarial conditions. 展开更多
关键词 scalable controlyet distribution network partitioning heterogeneous graph neural networks distributed energy resources communication infrastructures cyber attacks risk distributed energy resources ders voltage regulation
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Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
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作者 Pangwei Wang Jie Wang +2 位作者 Zipeng Wang Hangrui Dong Li Wang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1417-1435,共19页
Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The ... Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System(ITS).However,sensors are vulnerable to environmental interference,which can result in data loss at specific points or along arterial highways for certain periods,potentially undermining system safety and decision-making reliability.To address these challenges,a deep learning method based on Graph Convolutional Networks(GCN)and Gated Recurrent Units(GRU)is proposed,leveraging Artificial Intelligence(AI)and intelligent connected technologies for real-time acquisition of multi-sensor perception data.A feature-level fusion integrates multi-source perception data.GCN captures spatial dependencies from the road network topology,while GRU extracts temporal features from time series,enabling accurate imputation of missing traffic data.The method is evaluated at intelligent connected intersections in the Beijing High-level Autonomous Driving Demonstration Area.Results show that the accuracy of long-term traffic state completion reaches 89.36%,and the Root Mean Square Error(RMSE)is reduced by 17.2%compared to the Long Short-Term Memory(LSTM)baseline.This framework provides a practical solution for deploying traffic holographic perception technology in secure and trustworthy ITS. 展开更多
关键词 Intelligent transportation information security traffic information completion traffic holographic perception AI-driven edge computing graph convolutional neural network
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Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation 认领 引用
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作者 Xin Luo Fanghui Bi Tiantian He 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1642-1656,共15页
Quality of service(Qo S)data that characterize historical user-service invocations that vary over time are vital to discovering patterns of cloud services and understanding user behaviors.Though effective,prevalent ap... Quality of service(Qo S)data that characterize historical user-service invocations that vary over time are vital to discovering patterns of cloud services and understanding user behaviors.Though effective,prevalent approaches never consider higher-order spatiotemporal connectivity within Qo S data,thus suffering from inferior performance.To address this critical issue,this paper presents spatiotemporal graph convolutional network(GCN)that is equipped with the functionality of latent factorization of tensors(SGLFT).It is achieved by introducing three key innovations:1)Proposing a tensor graph convolution based on the generalized tensor product technique for uniformly modeling the temporal and spatial patterns within dynamic user-service graphs;2)Incorporating the built layer-wise graph convolution into tensor factorization for efficiently capturing the implied spatiotemporal high-order connectivity;and 3)Developing a nodelevel attention pooling mechanism to perceive feature differences among neighbors and across time slots.Theoretical derivations are conducted to demonstrate that the expressivity of the graph neural network proposed in this paper is evidently higher than that of vanilla GCNs.Empirical studies on eight large-scale testing cases arising from two real-world dynamic Qo S datasets show that SGLFT substantially outperforms state-of-the-art Qo S estimators regarding estimation accuracy for missing dynamic QoS data. 展开更多
关键词 Cloud service data science dynamic quality-of-service estimation graph convolutional networks(GCNs) latent factorization of tensors latent feature analysis non-euclidean data representation learning tensor product
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Spatio-Temporal Graph Neural Networks with Elastic-Band Transform for Solar Radiation Prediction 认领 引用 被引量：1
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作者 Guebin Choi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期848-872,共25页
This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically def... This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods. 展开更多
关键词 Spatio-temporal graph neural network(STGNN) elastic-band transform(EBT) solar radiation fore-casting spurious correlation time series decomposition
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A Multi-Scale Graph Neural Networks Ensemble Approach for Enhanced DDoS Detection 认领 引用
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作者 Noor Mueen Mohammed Ali Hayder Seyed Amin Hosseini Seno +2 位作者 Hamid Noori Davood Zabihzadeh Mehdi Ebady Manaa 《Computers, Materials & Continua》 SCIE EI 2026年第4期1216-1242,共27页
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t... Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist. 展开更多
关键词 DDoS detection graph neural networks multi-scale learning ensemble learning network security stealth attacks network graphs
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Voxel event graph neural network for event-based human gait recognition 认领 引用
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作者 Guangyuan MA Xiaolin GONG +2 位作者 Jiangtao XU Jiandong GAO Zhaoxuan GUO 《Optoelectronics Letters》 EI 2026年第3期167-173,共7页
To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the gr... To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the graph.Then the edges are constructed based on spatio-temporal distance and temporal order constraints so that the event stream is constructed as a graph structure.Finally,a lightweight feature extraction network based on graph neural networks(GNNs)is used to efficiently capture spatio-temporal information and motion cues from the event graph.To evaluate our method,an event-based gait recognition dataset called Celex-Gait is created.Experimental results show that VEGNN achieves a gait recognition accuracy of 95.8%and 93.9%on the Celex-Gait dataset and the DVS128-Gait-Day dataset,respectively.Furthermore,compared to the state-of-the-art methods,VEGNN reduces the number of model parameters by 30%.This indicates that VEGNN achieves higher recognition accuracy with lower model complexity. 展开更多
关键词 selects representative voxels voxel event graph neural network vegnn event stream lightweight feature extraction network voxel event graph neural network graph structurefinallya graph neural networks gnns gait recognitiona
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