期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到228篇文章
< 1 2 … 12 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Graph Convolution Network with EEG-EMG Fusion for Upper Limb Motion Intention Recognition 认领 引用
1
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
暂未订购 下载PDF
Prediction of mechanical properties of cross-linked polymer interface by graph convolution network 认领 引用
2
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
暂未订购 下载PDF
Spectral graph convolution networks for microbialite lithology identification based on conventional well logs 认领 引用
3
作者 Ke-Ran Li Jin-Min Song +9 位作者 Han Wang Hai-Jun Yan Shu-Gen Liu Yang Lan Xin Jin Jia-Xin Ren Ling-Li Zhao Li-Zhou Tian Hao-Shuang Deng Wei Chen 《Petroleum Science》 SCIE EI CAS CSCD 2025年第4期1513-1533,共21页
Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,... Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry. 展开更多
关键词 Graph convolution network Mirobialite Lithology forecasting Well log
暂未订购 下载PDF
Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems 认领 引用
4
作者 Sang-min Lee Namgi Kim 《Computers, Materials & Continua》 SCIE EI 2024年第2期1897-1914,共18页
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ... Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets. 展开更多
关键词 Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems
暂未订购 下载PDF
Modularized Graph Convolutional Network 认领 引用
5
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
暂未订购 下载PDF
Recommendation System Based on Perceptron and Graph Convolution Network 认领 引用
6
作者 Zuozheng Lian Yongchao Yin Haizhen Wang 《Computers, Materials & Continua》 SCIE EI 2024年第6期3939-3954,共16页
The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combinatio... The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms. 展开更多
关键词 Recommendation system graph convolution network attention mechanism multi-layer perceptron
暂未订购 下载PDF
Semantic-aware graph convolution network on multi-hop paths for link prediction 认领 引用 被引量：1
7
作者 彭斐 CHEN Shudong +2 位作者 QI Donglin YU Yong TONG Da 《High Technology Letters》 EI CAS 2023年第3期269-278,共10页
Knowledge graph(KG) link prediction aims to address the problem of missing multiple valid triples in KGs. Existing approaches either struggle to efficiently model the message passing process of multi-hop paths or lack... Knowledge graph(KG) link prediction aims to address the problem of missing multiple valid triples in KGs. Existing approaches either struggle to efficiently model the message passing process of multi-hop paths or lack transparency of model prediction principles. In this paper,a new graph convolutional network path semantic-aware graph convolution network(PSGCN) is proposed to achieve modeling the semantic information of multi-hop paths. PSGCN first uses a random walk strategy to obtain all-hop paths in KGs,then captures the semantics of the paths by Word2Sec and long shortterm memory(LSTM) models,and finally converts them into a potential representation for the graph convolution network(GCN) messaging process. PSGCN combines path-based inference methods and graph neural networks to achieve better interpretability and scalability. In addition,to ensure the robustness of the model,the value of the path thresholdKis experimented on the FB15K-237 and WN18RR datasets,and the final results prove the effectiveness of the model. 展开更多
关键词 knowledge graph(KG) link prediction graph convolution network(GCN) knowledge graph completion(KGC) multi-hop paths semantic information
暂未订购 下载PDF
Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks 认领 引用
8
作者 Yunchang Liu Fei Wan Chengwu Liang 《Computers, Materials & Continua》 SCIE EI 2024年第3期4343-4361,共19页
Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of... Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes.However,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial features.This paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow.By combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data.Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms. 展开更多
关键词 Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism
暂未订购 下载PDF
Self-attention Based Multimodule Fusion Graph Convolution Network for Traffic Flow Prediction 认领 引用
9
作者 Lijie Li Hongyang Shao +1 位作者 Junhao Chen Ye Wang 《国际计算机前沿大会会议论文集》 2022年第1期3-16,共14页
With rapid economic development,the per capita ownership of automobiles in our country has begun to rise year by year.More researchers have paid attention to using scientific methods to solve traffic flow problems.Tra... With rapid economic development,the per capita ownership of automobiles in our country has begun to rise year by year.More researchers have paid attention to using scientific methods to solve traffic flow problems.Traffic flow prediction is not simply affected by the number of vehicles,but also contains various complex factors,such as time,road conditions,and people flow.However,the existing methods ignore the complexity of road conditions and the correlation between individual nodes,which leads to the poor performance.In this study,a deep learning model SAMGCN is proposed to effectively capture the correlation between individual nodes to improve the performance of traffic flow prediction.First,the theory of spatiotemporal decoupling is used to divide each time of each node into finer particles.Second,multimodule fusion is used to mine the potential periodic relationships in the data.Finally,GRU is used to obtain the potential time relationship of the three modules.Extensive experiments were conducted on two traffic flow datasets,PeMS04 and PeMS08 in the Caltrans Performance Measurement System to prove the validity of the proposed model. 展开更多
关键词 Flow prediction Temporal-spatial correlation Graph convolution network Self-attention mechanism
PSTGCN:A Taxi Flow Prediction Model Period-Based Spatial-Temporal Graph Convolution Network 认领 引用
10
作者 Zhiqiang Lv Qu Hao +1 位作者 Jianbo Li Lei You 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第3期1706-1721,共16页
Efficient urban traffic management and intelligent urban planning are pivotal drivers for the development of modern cities.Among them,accurate prediction of taxi flow,as a core component of the urban transportation sy... Efficient urban traffic management and intelligent urban planning are pivotal drivers for the development of modern cities.Among them,accurate prediction of taxi flow,as a core component of the urban transportation system,has significant implications for traffic management,resource allocation,and the daily lives of citizens.However,practical taxi flow prediction faces numerous challenges,particularly in fully utilizing periodic features.To address the issue of capturing periodic characteristics in urban taxi flow prediction research,this study proposes a prediction model named Period-based Spatial-Temporal Graph Convolution Network(PSTGCN).In the PSTGCN,graph convolutional techniques are employed in the spatial convolution module to capture both local and global spatial correlations of taxi flow,effectively integrating the two.This further enhances the understanding of flow interaction between different areas.Furthermore,in the temporal convolution module,PSTGCN transforms the one-dimensional time series data into two-dimensional data,enabling the simultaneous analysis of both the within-period changes in taxi flow and the patterns of variation between different periods.This significantly strengthens the capability to capture flow periodic features.Lastly,this study extensively validates the predictive performance of the PSTGCN model.Experimental results demonstrate its significant advantages over various existing benchmark models in terms of prediction effectiveness.Moreover,the model's excellent generalization ability is showcased through validation using multiple real-world datasets. 展开更多
关键词 urban planning taxi flow periodic feature spatial-temporal model Graph Convolution Network(GCN)
暂未订购 下载PDF
Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks 认领 引用
11
作者 Motasem S.Alsawadi Miguel Rio 《Computers, Materials & Continua》 SCIE EI 2022年第6期4643-4658,共16页
Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the ... Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments. 展开更多
关键词 Skeleton split strategies spatial temporal graph convolutional neural networks skeleton joints action recognition
暂未订购 下载PDF
Large Language Model Assisted Interpretability in Graph Convolution Network for AGC Dispatch 认领 引用
12
作者 Xiaoshun Zhang Kun Zhang +1 位作者 Zhengxun Guo Penggen Wang 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2026年第2期632-643,共12页
Automatic generation control(AGC)dispatch is the key task of secondary frequency regulation for interconnected grids.To generate a high-quality dispatch solution,numerous machine learning techniques,such as reinforcem... Automatic generation control(AGC)dispatch is the key task of secondary frequency regulation for interconnected grids.To generate a high-quality dispatch solution,numerous machine learning techniques,such as reinforcement learning and graph convolutional networks(GCNs),have been developed for AGC dispatch.However,they are challenging to apply to a real-world power grid due to their weak interpretability.Hence,this work proposes a novel approach to large language model(LLM)-assisted interpretability in GCN for AGC dispatch.Firstly,the impact of input features(e.g.,the total regulation command and the regulation capacities of various resources)on dispatch solutions is assessed quantitatively using Shapley additive explanations(SHAP)for global interpretability.Then,local interpretability for GCN is achieved using an LLM-assisted,model-agnostic local interpretable model-agnostic explanations(LIME),which can provide actionable insights into the model's decision logic.SHAP shows that the top eight features drive decisions,while the rest average just 15.8%of the leading feature's contribution.Unit outputs correlate positively with their own history and negatively with others.Swapping LIME's linear model for a decision tree boosts multiple metrics by over 50%.Experimental results further confirm that this method not only clearly uncovers the relationships between input features and AGC dispatch outputs,but also faithfully reconstructs the GCN's decision logic across different dispatch scenarios. 展开更多
关键词 AGC dispatch graph convolution network interpretability large language model
暂未订购 下载PDF
Advanced High-Order Graph Convolutional Networks With Assorted Time-Frequency Transforms 认领 引用
13
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
暂未订购 下载PDF
A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification 认领 引用
14
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://gffzz188fe103f8f1460asx6nqufqkbfk566wk.ffgz.tsg.suse.edu.cn/jenny3489/BTDGC. 展开更多
关键词 convolution graph convolutional network masking mechanism transforms
暂未订购 下载PDF
Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
15
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
暂未订购 下载PDF
HGS-ATD:A Hybrid Graph Convolutional Network-GraphSAGE Model for Anomaly Traffic Detection 认领 引用
16
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
暂未订购 下载PDF
Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks 认领 引用
17
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
暂未订购 下载PDF
Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation 认领 引用
18
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
暂未订购 下载PDF
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
19
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
暂未订购 下载PDF
Self-FAGCFN:Graph-Convolution Fusion Network Based on Feature Fusion and Self-Supervised Feature Alignment for Pneumonia and Tuberculosis Diagnosis 认领 引用 被引量：1
20
作者 Junding Sun Wenhao Tang +5 位作者 Lei Zhao Chaosheng Tang Xiaosheng Wu Zhaozhao Xu Bin Pu Yudong Zhang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期2012-2029,共18页
Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely us... Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications. 展开更多
关键词 Feature fusion Self-supervised feature alignment Convolutional neural networks Graph convolutional networks Class imbalance Feature-centroid fusion
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 12 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈