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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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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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Dual Channel Graph Convolutional Networks via Personalized PageRank 认领 引用
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作者 Longlong Lin Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期221-223,共3页
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat... Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications. 展开更多
关键词 convolutional node feature similarity graph convolutional framework learning graph representations neural networks gnns networks graph personalized
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MSSTGCN: Multi-Head Self-Attention and Spatial-Temporal Graph Convolutional Network for Multi-Scale Traffic Flow Prediction 认领 引用 被引量：2
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作者 Xinlu Zong Fan Yu +1 位作者 Zhen Chen Xue Xia 《Computers, Materials & Continua》 SCIE EI 2025年第2期3517-3537,共21页
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ... Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks. 展开更多
关键词 Graph convolutional network traffic flow prediction multi-scale traffic flow spatial-temporal model
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Advanced High-Order Graph Convolutional Networks With Assorted Time-Frequency Transforms 认领 引用
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作者 Ling Wang Ye Yuan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期394-408,共15页
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa... A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance. 展开更多
关键词 Dynamic graph(DG)learning ensemble graph representation learning high-order graph convolution network(HGCN) time-frequency transform tensor product
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HGS-ATD:A Hybrid Graph Convolutional Network-GraphSAGE Model for Anomaly Traffic Detection 认领 引用
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作者 Zhian Cui Hailong Li Xieyang Shen 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第1期33-50,共18页
With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a ... With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks. 展开更多
关键词 anomaly traffic detection graph neural network deep learning graph convolutional network
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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional Networks 认领 引用 被引量：1
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作者 Baoquan Liu Xi Chen +2 位作者 Qingjun Yuan Degang Li Chunxiang Gu 《Computers, Materials & Continua》 SCIE EI 2025年第2期3179-3201,共23页
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based... With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%. 展开更多
关键词 Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks
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SGP-GCN:A Spatial-Geological Perception Graph Convolutional Neural Network for Long-Term Petroleum Production Forecasting 认领 引用
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作者 Xin Liu Meng Sun +1 位作者 Bo Lin Shibo Gu 《Energy Engineering》 EI 2025年第3期1053-1072,共20页
Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecas... Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells. 展开更多
关键词 Petroleum production forecast graph convolutional neural networks(GCNs) spatial-geological rela-tionships production clustering attention mechanism
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AG-GCN: Vehicle Re-Identification Based on Attention-Guided Graph Convolutional Network 认领 引用
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作者 Ya-Jie Sun Li-Wei Qiao Sai Ji 《Computers, Materials & Continua》 SCIE EI 2025年第7期1769-1785,共17页
Vehicle re-identification involves matching images of vehicles across varying camera views.The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-c... Vehicle re-identification involves matching images of vehicles across varying camera views.The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images,which increases the complexity of re-identification tasks.To tackle these challenges,this study proposes AG-GCN(Attention-Guided Graph Convolutional Network),a novel framework integrating several pivotal components.Initially,AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically,thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones.Moreover,AG-GCN adopts a graph-based structure to encapsulate deep local features.A graph convolutional network then amalgamates these features to understand the relationships among vehicle-related characteristics.Subsequently,we amalgamate feature maps from both the attention and graph-based branches for a more comprehensive representation of vehicle features.The framework then gauges feature similarities and ranks them,thus enhancing the accuracy of vehicle re-identification.Comprehensive qualitative and quantitative analyses on two publicly available datasets verify the efficacy of AG-GCN in addressing intra-class and inter-class variability issues. 展开更多
关键词 Vehicle re-identification a lightweight attention module global features local features graph convolution 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://gffzz188fe103f8f1460asnqx5wncfokq66wcb.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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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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Fault Identification Method for In-Core Self-Powered Neutron Detectors Combining Graph Convolutional Network and Stacking Ensemble Learning 认领 引用 被引量：2
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作者 LIN Weiqing LU Yanzhen +1 位作者 MIAO Xiren QIU Xinghua 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期1018-1027,共10页
Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification ... Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels. 展开更多
关键词 self-powered neutron detector(SPND) graph convolutional network(GCN) Stacking ensemble learning fault identification
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Integration of Federated Learning and Graph Convolutional Networks for Movie Recommendation Systems 认领 引用 被引量：2
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作者 Sony Peng Sophort Siet +3 位作者 Ilkhomjon Sadriddinov Dae-Young Kim Kyuwon Park Doo-Soon Park 《Computers, Materials & Continua》 SCIE EI 2025年第5期2041-2057,共17页
Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that lever... Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate recommendations.However,it struggles with challenges like the cold-start problem,scalability issues,and data sparsity.To address these limitations,we develop a Graph Convolutional Networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may overlook.We integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized datasets.This not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their data.Additionally,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user data.We validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems. 展开更多
关键词 Recommendation systems collaborative filtering graph convolutional networks federated learning framework
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Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme 认领 引用 被引量：1
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作者 Zhuo Chen Bowen Zhu Chuan Zhou 《Digital Communications and Networks》 SCIE EI CSCD 2025年第1期60-70,共11页
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat... Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods. 展开更多
关键词 Edge computing Network virtualization Container cluster Deep reinforcement learning Graph convolutional network
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Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks 认领 引用
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作者 Wasim Khan Afsaruddin Mohd +3 位作者 Mohammad Suaib Mohammad Ishrat Anwar Ahamed Shaikh Syed Mohd Faisal 《Data Science and Management》 EI CSCD 2025年第2期137-146,共10页
In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study in... In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism,thereby creating a more nuanced and efficient method for anomaly detection in complex networks.The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data.This is further bolstered by deep residual learning,which is employed to model intricate nonlinear connections directly from input data.A pivotal innovation in our approach is the incorporation of a residual-based attention mech-anism.This mechanism dynamically adjusts the importance of nodes based on their residual information,thereby significantly enhancing the sensitivity of the model to subtle anomalies.Furthermore,we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data.This mapping is the key to our model’s ability to pinpoint anomalies with greater precision.An extensive experimental setup was used to validate the efficacy of the proposed model.Using attributed social network datasets,we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection.The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks. 展开更多
关键词 Anomaly detection Deep learning Hypersphere learning Residual modeling Graph convolution network Attention mechanism
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Occluded Gait Emotion Recognition Based on Multi-Scale Suppression Graph Convolutional Network 认领 引用
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作者 Yuxiang Zou Ning He +2 位作者 Jiwu Sun Xunrui Huang Wenhua Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期1255-1276,共22页
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac... In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods. 展开更多
关键词 KNN interpolation multi-scale temporal convolution suppression graph convolutional network gait emotion recognition human skeleton
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Graph Convolution Network with EEG-EMG Fusion for Upper Limb Motion Intention Recognition 认领 引用
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作者 ZHENG Luzhou ZHAO Changchen +2 位作者 ZHANG Chao CHENG Shichao ZHANG Jianhai 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第1期12-23,共12页
With the continuous advancement of sensors and algorithms,an increasing number of deep learning methods have been applied to fine-grained upper limb motion intention recognition using multimodal physiological signals.... With the continuous advancement of sensors and algorithms,an increasing number of deep learning methods have been applied to fine-grained upper limb motion intention recognition using multimodal physiological signals.However,effectively and quantifiably integrating correlations between electroencephalogram(EEG)and electromyogram(EMG)signal channels as well as within EEG signal channels as a clue to improve performance remained challenging.In this paper,we proposed a novel framework that achieved accurate prediction of upper limb motion intentions via fusing EEG and EMG signals.Firstly,the raw input signals were fed into the feature extraction module,respectively,enabling feature decomposition in the channel dimension.Secondly,the graph convolution module with learnable edge weights was proposed to adaptively learn correlations between different modalities.Thirdly,we designed a self-attention graph pooling module that employed the self-attention mechanism to compute the attention score for each node as the basis for pooling.Compared with calculation methods using the mean or maximum value,this approach was more likely to retain nodes with stronger correlations to motor intentions.Finally,the prediction results were obtained through a classifier.We validated the effectiveness of our method on a publicly available multimodal upper limb dataset,achieving an accuracy of 93.17%. 展开更多
关键词 motion intention recognition graph convolution network(GCN) electroencephalogram(EEG) electromyogram(EMG) multi-modality
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Prediction of mechanical properties of cross-linked polymer interface by graph convolution network 认领 引用
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作者 Xintianyang Wang Lijuan Liao +1 位作者 Chenguang Huang Xianqian Wu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期404-424,共21页
Machine learning models have made significant advances in the establishment of structure-property relationships.However,it is still a challenge to predict the mechanical properties of the adhesive interface due to the... Machine learning models have made significant advances in the establishment of structure-property relationships.However,it is still a challenge to predict the mechanical properties of the adhesive interface due to the complexity and randomness of the polymer topologies.In this paper,we employed a graph convolutional network(GCN)model to predict the mechanical properties of a specific cross-linked polymer interfacial system,including yield strength(σy),ultimate strength(σu),failure strain(εu),and fracture toughness(Γ)utilizing molecular dynamics simulations.The results showed that the adopted GCN model can predict the mechanical properties with over 88%accuracy.Furthermore,the prediction performances for εu and σu are better than those for Γ and σy,with R2~0.73 for εu,R2~0.64 for σu,R2~0.51 for Γ,and R2~0.43 for σy.It is worth noting that the GCN model with the sum aggregator slightly outperforms that with the mean aggregator,and that models with linear regression and fully connected neural network regression provide similar predictions.The influence of input node features on prediction performance was also investigated.It was observed that the node closeness centrality is an important graph parameter in prediction.Specifically,node closeness centrality presents a more significant influence on the global mechanical properties of the adhesive interface,such as εu,σu,and Γ.Additionally,sensitivity analysis demonstrated that appropriate hyperparameters can improve computational efficiency without losing accuracy on a restricted set of data.This paper demonstrated the capacity of the GCN model to predict the mechanical properties of the adhesive interface with diverse topologies and provided a possible pathway for improving the mechanical properties of the adhesive interface by tailoring polymer structures in the future. 展开更多
关键词 Graph convolutional network Molecular dynamic Adhesive interface Mechanical properties Topology structure
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A Graph-Based Spatio-Temporal Attention Network for Stress-Strain Behavior Prediction of Copper-Based Composites 认领 引用
19
作者 Chuhan Zhang Jinguo You +5 位作者 Jialin Xu Mingqian Li Xiaofeng Chen Jingmei Tao Caiju Li Jianhong Yi 《Computers, Materials & Continua》 SCIE EI 2026年第7期407-422,共16页
With the rapid development of artificial intelligence and data-driven modeling,deep learning has become an effective tool for analyzing scientific discovery such as predicting material behaviors.Graphene-reinforced co... With the rapid development of artificial intelligence and data-driven modeling,deep learning has become an effective tool for analyzing scientific discovery such as predicting material behaviors.Graphene-reinforced copper-based composites,which exhibit excellent mechanical,electrical,and thermal properties,have attracted extensive attention in advanced engineering applications;however,accurate prediction of their stress-strain behavior still relies heavily on computationally expensive molecular dynamics simulations or experiments.In this work,we propose a Graph-based Spatio-Temporal Attention Network,termed GraphSTAN,for stress-strain behavior prediction of copper-based composites.Specifically,atomic-scale initial microstructures are encoded as graphs and integrated with static physical parameters.A topology-aware spatio-temporal feature interaction mechanism is introduced to effectively couple structural representations with stress-strain time-series dynamics,enabling accurate prediction of full stress-strain evolution.Moreover,a multi-features dataset is constructed based on LAMMPS molecular dynamics simulations,consisting of 596 independent simulation samples corresponding to distinct combinations of microstructural configurations,loading conditions,and stress-strain time series.Experimental results demonstrate that GraphSTAN effectively predicts full stress-strain curves and achieves the higher performance of R2,MAE and RMSE for yield strength and Young’s modulus,respectively,significantly outperforming baseline methods. 展开更多
关键词 Temporal convolutional network graph attention network copper-based composites mechanical property prediction stress-strain behavior
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Log-Based Anomaly Detection of System Logs Using Graph Neural Network 认领 引用
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作者 Eman Alsalmi Abeer Alhuzali Areej Alhothali 《Computers, Materials & Continua》 SCIE EI 2026年第2期1265-1284,共20页
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur... Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores. 展开更多
关键词 Log anomaly detection BERT graph convolutional network systemlogs explainable anomaly detection
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