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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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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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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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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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Aspect-Level Sentiment Analysis of Bi-Graph Convolutional Networks Based on Enhanced Syntactic Structural Information 认领 引用
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作者 Junpeng Hu Yegang Li 《Journal of Computer and Communications》 2025年第1期72-89,共18页
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep... Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter. 展开更多
关键词 Aspect-Level Sentiment Analysis Sentiment Knowledge Multi-Head Attention Mechanism Graph Convolutional Networks
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Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey 认领 引用 被引量：6
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作者 Xin Liu Mingyu Yan +3 位作者 Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第2期205-234,共30页
Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph representations.Although GCN performs well compared with other meth... Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph representations.Although GCN performs well compared with other methods,it still faces challenges.Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs.Therefore,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant effect.In this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN.To highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories.Finally,we discuss some challenges and future research directions of the sampling methods. 展开更多
关键词 Efficient training graph convolutional networks(GCNs) graph neural networks(GNNs) sampling method
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Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation 认领 引用 被引量：3
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作者 Ao Feng Zhengjie Gao +3 位作者 Xinyu Song Ke Ke Tianhao Xu Xuelei Zhang 《Computers, Materials & Continua》 SCIE EI 2020年第8期909-923,共15页
Existing solutions do not work well when multi-targets coexist in a sentence.The reason is that the existing solution is usually to separate multiple targets and process them separately.If the original sentence has N ... Existing solutions do not work well when multi-targets coexist in a sentence.The reason is that the existing solution is usually to separate multiple targets and process them separately.If the original sentence has N target,the original sentence will be repeated for N times,and only one target will be processed each time.To some extent,this approach degenerates the fine-grained sentiment classification task into the sentence-level sentiment classification task,and the research method of processing the target separately ignores the internal relation and interaction between the targets.Based on the above considerations,we proposes to use Graph Convolutional Network(GCN)to model and process multi-targets appearing in sentences at the same time based on the positional relationship,and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words.In addition to the standard target-dependent sentiment classification task,an auxiliary node relation classification task is constructed.Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets:SemEval-2014 Task 4,i.e.,reviews for restaurants and laptops.Furthermore,the method of dividing the target words into isolated individuals has disadvantages,and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model. 展开更多
关键词 Deep learning sentiment analysis graph convolutional networks(GCN)
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Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection 认领 引用 被引量：1
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作者 Kun Ding Lu Xu +5 位作者 Ming Liu Xiaoxiong Zhang Liu Liu Daojian Zeng Yuting Liu Chen Jin 《Computers, Materials & Continua》 SCIE EI 2023年第1期641-654,共14页
Event detection(ED)is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word m... Event detection(ED)is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word most clearly expressing event occurrence.Thus,current approaches require both annotated triggers as well as event types in training data.Nevertheless,triggers are non-essential in ED,and it is time-wasting for annotators to identify the“most clearly”word from a sentence,particularly in longer sentences.To decrease manual effort,we evaluate event detectionwithout triggers.We propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks(TA-GCN)for event detection.Specifically,the task is identified as a multi-label classification problem.We first encode the input sentence using a novel type-aware neural network with attention mechanisms.Then,a Graph Convolutional Networks(GCN)-based multilabel classification model is exploited for event detection.Experimental results demonstrate the effectiveness. 展开更多
关键词 Event detection information extraction type-aware attention graph convolutional networks
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A malware propagation prediction model based on representation learning and graph convolutional networks 认领 引用
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作者 Tun Li Yanbing Liu +3 位作者 Qilie Liu Wei Xu Yunpeng Xiao Hong Liu 《Digital Communications and Networks》 SCIE CSCD 2023年第5期1090-1100,共11页
The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of netw... The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of network structure,diversity of network nodes,and sparsity of data all pose difficulties in predicting propagation.This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN)to address the aforementioned problems.First,to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network,a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information.The influence of the relationship between nodes on the infection intensity is also analyzed.Second,given the diversity and complexity of the content and structure of infected and normal nodes in the network,considering the advantages of representation learning in data feature extraction,the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes.This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional,dense,and real-valued representation learning for the characteristics of propagation spatial data.We also design a new method,Tensor2vec,to learn the potential structural features of malware propagation.Finally,considering the convolution ability of GCN for non-Euclidean data,we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier.The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation. 展开更多
关键词 Malware Representation learning Graph convolutional networks(GCN) Tensor decomposition Propagation prediction
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Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks 认领 引用 被引量：2
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作者 Binhui Tang Junfeng Wang +3 位作者 Huanran Qiu Jian Yu Zhongkun Yu Shijia Liu 《Computers, Materials & Continua》 SCIE EI 2023年第1期235-252,共18页
The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cy... The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text. 展开更多
关键词 Attack behavior extraction cyber threat intelligence(CTI) graph convolutional network(GCN) heterogeneous textual network(HTN)
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Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction 认领 引用 被引量：2
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作者 Chuyuan Wei Jinzhe Li +2 位作者 Zhiyuan Wang Shanshan Wan Maozu Guo 《Computers, Materials & Continua》 SCIE EI 2024年第5期3299-3314,共16页
Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,... Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous. 展开更多
关键词 Relation extraction graph convolutional neural networks dependency tree dynamic structure attention
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SGG-DGCN:Wind Turbine Anomaly Identification by Using Deep Graph Convolutional Networks with Similarity Graph Generation Strategy 认领 引用
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作者 Xiaomin Wang Di Zhou +2 位作者 Xiao Zhuang Jian Ge and Jiawei Xiang 《Journal of Dynamics, Monitoring and Diagnostics》 2024年第4期258-267,共10页
In order to minimize wind turbine failures,fault diagnosis of wind turbines is becoming increasinglyimportant,deep learning methods excel at multivariate monitoring and data modeling,but they are often limited toEucli... In order to minimize wind turbine failures,fault diagnosis of wind turbines is becoming increasinglyimportant,deep learning methods excel at multivariate monitoring and data modeling,but they are often limited toEuclidean space and struggle to capture the complex coupling between wind turbine sensors.To addressthis problem,we convert SCADA data into graph data,where sensors act as nodes and their topologicalconnections act as edges,to represent these complex relationships more efficiently.Specifically,a wind turbineanomaly identification method based on deep graph convolutional neural network using similarity graphgeneration strategy(SGG-DGCN)is proposed.Firstly,a plurality of similarity graphs containing similarityinformation between nodes are generated by different distance metrics.Then,the generated similarity graphs arefused using the proposed similarity graph generation strategy.Finally,the fused similarity graphs are fed into theDGCN model for anomaly identification.To verify the effectiveness of the proposed SGG-DGCN model,we conducted a large number of experiments.The experimental results show that the proposed SGG-DGCNmodel has the highest accuracy compared with other models.In addition,the results of ablation experimentalso demonstrate that the proposed SGG strategy can effectively improve the accuracy of WT anomalyidentification. 展开更多
关键词 anomaly identification deep graph convolutional networks similarity graph generation wind turbine
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End-to-end aspect category sentiment analysis based on type graph convolutional networks 认领 引用
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作者 邵清 ZHANG Wenshuang WANG Shaojun 《High Technology Letters》 EI CAS 2023年第3期325-334,共10页
For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural net... For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model. 展开更多
关键词 aspect-based sentiment analysis(ABSA) bidirectional encoder representation from transformers(BERT) type graph convolutional network(TGCN) aspect category and senti-ment pair extraction
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Prediction Model for Traffic Flow with Missing Values Based on Generative Adversarial and Graph Convolutional Networks 认领 引用
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作者 CHEN Jian-zhong LÜZe-kai LIN Hao-meng 《Journal of Highway and Transportation Research and Development(English Edition)》 2023年第3期62-74,共13页
In order to improve the accuracy of urban road network traffic flow prediction with missing values,the generator and discriminator of the generative adversarial network are reconstructed,the loss function is improved,... In order to improve the accuracy of urban road network traffic flow prediction with missing values,the generator and discriminator of the generative adversarial network are reconstructed,the loss function is improved,and the traffic generative adversarial imputation network(TGAIN)is proposed for the completion of the missing data of traffic flow.Based on empirical mode decomposition(EMD),graph convolutional networks(GCN)and gated recurrent unit(GRU),EMD-GCN-GRU model is designed for urban road network traffic flow prediction.First,the traffic flow data is processed by empirical mode decomposition and each component of the same level is reconstructed as the input of the subsequent prediction model.Then,the graph convolutional networks are used to learn the road network topology to capture the spatial characteristic of traffic flow,and the gated recurrent unit is employed to capture the temporal characteristic of traffic flow.For the road network traffic flow data with missing values,TGAIN is used to complete the data,and then EMD-GCN-GRU is used to predict the traffic flow.The Shenzhen average vehicle speed data set is used to construct a variety of typical traffic flow data with different missing patterns and different missing rates to simulate the actual missing situation.The effectiveness of the method is verified on the ModelArts development platform.The results show that compared with the commonly used matrix factorization imputation method,the TGAIN model has higher completion accuracy in the random missing mode of the dataset and has better completion performance when the non-random missing rate is lower than 50%.Compared with other seven prediction algorithms,the proposed prediction method has higher prediction accuracy.Combining the data imputation method TGAIN with the traffic flow prediction method EMDGCN-GRU for urban road network traffic flow prediction with missing values can significantly reduce the negative impact of missing data and data noise on traffic flow prediction and capture the spatial and temporal correlation of network traffic flow,which improves the accuracy of urban road network traffic flow prediction. 展开更多
关键词 ITS traffic flow prediction generative adversarial imputation network urban road network graph convolutional networks
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Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition 认领 引用 被引量：2
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作者 Motasem S.Alsawadi El-Sayed M.El-kenawy Miguel Rio 《Computers, Materials & Continua》 SCIE EI 2023年第1期19-36,共18页
The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extrac... The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extract knowledge from these sources is imperative.Recently,the BlazePose system has been released for skeleton extraction from images oriented to mobile devices.With this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action.We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks.Hence,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition.Moreover,we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor.Additionally,we propose different skeleton detection thresholds that can improve the accuracy performance even further.We reached a top-1 accuracy performance of 40.1%on the Kinetics dataset.For the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively. 展开更多
关键词 Action recognition BlazePose graph neural network OpenPose skeleton spatial temporal graph convolution network
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Distributed Photovoltaic Real-Time Output Estimation Based on Graph Convolutional Networks 认领 引用 被引量：1
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作者 陈利跃 洪道鉴 +5 位作者 何星 卢东祁 张乾 谢妮娜 徐一洲 应煌浩 《Journal of Shanghai Jiaotong university(Science)》 EI 2024年第2期290-296,共7页
The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power st... The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy. 展开更多
关键词 distributed photovoltaic(PV) graph convolution network power estimation
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Multi-Label Image Classification Based on Object Detection and Dynamic Graph Convolutional Networks 认领 引用
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作者 Xiaoyu Liu Yong Hu 《Computers, Materials & Continua》 SCIE EI 2024年第9期4413-4432,共20页
Multi-label image classification is recognized as an important task within the field of computer vision,a discipline that has experienced a significant escalation in research endeavors in recent years.The widespread a... Multi-label image classification is recognized as an important task within the field of computer vision,a discipline that has experienced a significant escalation in research endeavors in recent years.The widespread adoption of convolutional neural networks(CNNs)has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification.However,inmulti-label image classification tasks,it is crucial to consider the correlation between labels.In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features,many existing studies use graph convolutional networks(GCN)for modeling.Object detection and multi-label image classification exhibit a degree of conceptual overlap;however,the integration of these two tasks within a unified framework has been relatively underexplored in the existing literature.In this paper,we come up with Object-GCN framework,a model combining object detection network YOLOv5 and graph convolutional network,and we carry out a thorough experimental analysis using a range of well-established public datasets.The designed framework Object-GCN achieves significantly better performance than existing studies in public datasets COCO2014,VOC2007,VOC2012.The final results achieved are 86.9%,96.7%,and 96.3%mean Average Precision(mAP)across the three datasets. 展开更多
关键词 Deep learning multi-label image recognition object detection graph convolution networks
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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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BiGraph-DTA:Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks 认领 引用
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作者 Arief Sartono Bambang Riyanto Trilaksono +2 位作者 Sophi Damayanti Anto Satriyo Nugroho Firdayani Firdayani 《Quantitative Biology》 CAS CSCD 2026年第1期129-151,共23页
Predicting drug-target affinity(DTA)is critical for discovering and developing hepatoprotective agents that can prevent and treat liver diseases.In this study,we propose BiGraph-DTA,a new predictive model for identify... Predicting drug-target affinity(DTA)is critical for discovering and developing hepatoprotective agents that can prevent and treat liver diseases.In this study,we propose BiGraph-DTA,a new predictive model for identifying DTA score prediction for hepatoprotective compounds by combining graph convolutional networks and bidirectional long short-term memory networks.This model is based on powerful frameworks that process both graph representations of molecular structures and sequential information from protein sequences to capture complex dependencies and interactions.Leveraging a curated hepatoprotective dataset(from ChEMBL)consisting of 21,421 interactions,the model outperforms traditional machine learning methods(such as random forest and XGBoost)as well as other deep learning methods(such as DeepDTA and GraphDTA)in terms of predictive performance.The BiGraph-DTA obtained the best mean squared error of 0.7885,R2 of 0.7208,and concordance index of 0.8508.Our proposed architecture holds potential for accelerating the drug discovery process of hepatoprotective therapy by highlighting the framework through which candidate drugs and their corresponding protein targets can be identified based on robust data-driven knowledge.This model,therefore,provides a new opportunity for discovering new hepatoprotective compounds,which may also make it possible to speed up finding new liver disease drugs. 展开更多
关键词 BiGraph-DTA drug discovery drug-target affinity score prediction graph convolutional network(GCN) hepatoprotective agents
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