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Spatiotemporal Data Graph Modeling and Exploration of Application Scenarios in “Power Grid One Graph” 认领 引用 被引量：8
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作者 Peng Li Zhen Dai +4 位作者 Yachen Tang Guangyi Liu Jiaxuan Hou Qinyu Feng Quanchen Lin 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2025年第2期538-551,共14页
By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compa... By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compared to the bus-branch model, the node-breaker model provides higher granularity in describing grid components and can dynamically reflect changes in equipment status, thus improving the efficiency of grid dispatching and operation. This paper proposes a spatiotemporal data modeling method based on a graph database. It elaborates on constructing graph nodes, graph ontology models, and graph entity models from grid dispatch data, describing the construction of the spatiotemporal node-breaker graph model and the transformation to the bus-branch model. Subsequently, by integrating spatiotemporal data attributes into the pre-built static grid graph model, a spatiotemporal evolving graph of the power grid is constructed. Furthermore, the concept of the “Power Grid One Graph” and its requirements in modern power systems are elucidated. Leveraging the constructed spatiotemporal node-breaker graph model and graph computing technology, the paper explores the feasibility of grid situational awareness. Finally, typical applications in an operational provincial grid are showcased, and potential scenarios of the proposed spatiotemporal graph model are discussed. 展开更多
关键词 “Power Grid One Graph” graph data modeling situational awareness spatiotemporal evolving graph spatiotemporal node-breaker graph model
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DVG-GNN:Dual-View Graph Representation Learning for Encrypted Traffic Classification 认领 引用
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作者 Guan Yang Haozhen Wang +2 位作者 Yu Wang Weiguang Liu Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第10期783-806,共24页
The rapid proliferation of encrypted communication technologies,such as TLS,VPNs,and Tor,has significantly limited the effectiveness of traditional traffic classification methods that rely on port numbers or deep pack... The rapid proliferation of encrypted communication technologies,such as TLS,VPNs,and Tor,has significantly limited the effectiveness of traditional traffic classification methods that rely on port numbers or deep packet inspection.While handcrafted statistical features provide partial solutions,they often lack robustness and generalization in complex traffic scenarios.Although deep learning models such as CNNs and RNNs can capture local and sequential patterns,they typically overlook higher-order structural dependencies among bytes.To address these challenges,we propose DVG-GNN,a Dual-View Graph representation learning framework for encrypted traffic classification.The framework decomposes each packet into header and payload views,leveraging distinct byte embeddings and multi-scale one-dimensional convolutions to extract fine-grained contextual features.View-specific graphs are constructed using Pointwise Mutual Information(PMI)to model byte-level relationships,and a GraphSAGE-based encoder with attention pooling is employed to learn global structural representations.A gated fusion mechanism further integrates the dual-view features to enhance discriminative capability.Extensive experiments on ISCX-VPN2016,ISCX-Tor2016,and USTC-TFC2016 demonstrate that the proposed method consistently outperforms state-of-the-art approaches across multiple evaluation metrics,validating its effectiveness and generalization ability in diverse encrypted traffic classification tasks. 展开更多
关键词 Encrypted traffic classification graph neural networks dual-view representation learning pointwise mutual information traffic graph modeling
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Thermal-hydraulic modeling and analysis of hydraulic system by pseudo-bond graph 认领 引用 被引量：5
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作者 胡均平 李科军 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第7期2578-2585,共8页
To increase the efficiency and reliability of the thermodynamics analysis of the hydraulic system, the method based on pseudo-bond graph is introduced. According to the working mechanism of hydraulic components, they ... To increase the efficiency and reliability of the thermodynamics analysis of the hydraulic system, the method based on pseudo-bond graph is introduced. According to the working mechanism of hydraulic components, they can be separated into two categories: capacitive components and resistive components. Then, the thermal-hydraulic pseudo-bond graphs of capacitive C element and resistance R element were developed, based on the conservation of mass and energy. Subsequently, the connection rule for the pseudo-bond graph elements and the method to construct the complete thermal-hydraulic system model were proposed. On the basis of heat transfer analysis of a typical hydraulic circuit containing a piston pump, the lumped parameter mathematical model of the system was given. The good agreement between the simulation results and experimental data demonstrates the validity of the modeling method. 展开更多
关键词 thermodynamics hydraulic system pseudo-bond graph piston pump modeling temperature simulation
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Ontology Matching Method Based on Gated Graph Attention Model 认领 引用
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作者 Mei Chen Yunsheng Xu +1 位作者 Nan Wu Ying Pan 《Computers, Materials & Continua》 SCIE EI 2025年第3期5307-5324,共18页
With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms o... With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec*method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching. 展开更多
关键词 Ontology matching representation learning OWL2Vec*method graph attention model
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Industry-University-Research collaboration networks:the identification and driving factors of key technologies 认领 引用
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作者 Qining Peng Xian Zhang Zhenkang Fu 《Journal of Data and Information Science》 CSCD 2026年第1期146-174,共29页
Purpose:This study aims to analyze the key technologies in Industry–University–Research(IUR)cooperation within higher education institutions,deepen the understanding of the mechanisms of IUR cooperation and the proc... Purpose:This study aims to analyze the key technologies in Industry–University–Research(IUR)cooperation within higher education institutions,deepen the understanding of the mechanisms of IUR cooperation and the process of technological innovation,and reveal the dynamic evolution patterns and driving mechanisms of key technologies in IUR cooperation alliance networks at different stages.It also provides clear directions and strategic recommendations for cooperation among universities,enterprises,and research institutions.Methodology:This study uses patents applied for through IUR cooperation by Chinese Double First-Class universities from 2015 to 2024 as the data basis and employs the Louvain algorithm to divide IUR cooperation applicants.Subsequently,a Technology–Applicant network is constructed at two-year intervals,and key technologies are extracted using network information entropy.The evolution paths of technological characteristics are then thoroughly analyzed.Finally,the study proposes three hypotheses and employs the Exponential Random Graph Model(ERGM)to systematically elucidate the endogenous driving mechanisms of key technology characteristics in the applicant.Findings:Over the past decade,IUR cooperation in Chinese Double First-Class universities has undergone a transformation from single technological fields to the deep integration of multiple technological fields and from traditional application areas to emerging ones.The knowledge depth,knowledge width,and knowledge combination capabilities of IUR applicants,as core independent variables,have had varying impacts on network formation across different time periods.Among them,knowledge combination capability has played a significant role in promoting network formation.Research limitations:On the one hand,this study mainly focuses on the Double First-Class universities in China and does not cover other types of universities.On the other hand,while the study mainly focuses on the analysis of the IUR technology network,the analysis of the cooperation network between applicants is still insufficient.Practical implications:This study provides practical guidance for optimizing IUR cooperation networks by emphasizing the integration of multiple technological fields,balancing knowledge depth and width,enhancing knowledge combination ability,and optimizing the internal network structure.These measures help to strengthen the stability and efficiency of cooperation networks,boost innovative outcomes,and provide strong support for scientific and technological progress as well as economic development.Originality/value:This study examines the evolution of key technologies and their impact on IUR cooperation networks in China over ten years.It shows a shift from single to multiple technological fields and from traditional to emerging applications,highlighting Chinese global competitiveness.Core variables like knowledge depth,width,and combination ability differently affect network formation over time,with knowledge combination being consistently significant.Network structural characteristics also crucially regulate stability and efficiency.The findings offer theory-based practical guidance to optimize these networks. 展开更多
关键词 Industry–University–Research(IUR)collaboration networks Key technologies Bipartite network Information entropy Exponential Random Graph Model(ERGM)
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Layout graph model for semantic façade reconstruction using laser point clouds 认领 引用 被引量：4
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作者 Hongchao Fan Yuefeng Wang Jianya Gong 《Geo-Spatial Information Science》 SCIE EI CSCD 2021年第3期403-421,共19页
Building façades can feature different patterns depending on the architectural style,function-ality,and size of the buildings;therefore,reconstructing these façades can be complicated.In particular,when sema... Building façades can feature different patterns depending on the architectural style,function-ality,and size of the buildings;therefore,reconstructing these façades can be complicated.In particular,when semantic façades are reconstructed from point cloud data,uneven point density and noise make it difficult to accurately determine the façade structure.When inves-tigating façade layouts,Gestalt principles can be applied to cluster visually similar floors and façade elements,allowing for a more intuitive interpretation of façade structures.We propose a novel model for describing façade structures,namely the layout graph model,which involves a compound graph with two structure levels.In the proposed model,similar façade elements such as windows are first grouped into clusters.A down-layout graph is then formed using this cluster as a node and by combining intra-and inter-cluster spacings as the edges.Second,a top-layout graph is formed by clustering similar floors.By extracting relevant parameters from this model,we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling.Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method.The experimental results show that the proposed method achieves an average accuracy of 86.35%.Owing to its flexibility,the proposed layout graph model can deal with different types of façades and qualities of point cloud data,enabling a more robust and accurate reconstruc-tion of façade models. 展开更多
关键词 Building façade semantic reconstruction point cloud compound graph model stochastic process
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Who Affects Whom:Intercity Networks of Innovation and High-skilled Labor Migration 认领 引用
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作者 JIANG Jing AO Rongjun +3 位作者 CHEN Jing ZHOU Xiaoqi HOU Chunguang YAN Jinbo 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第8期1487-1503,共17页
An increasing number of studies confirm that there is a mutually reinforcing relationship between innovation development and high-skilled labor agglomeration within cities.Between cities,however,the relationship betwe... An increasing number of studies confirm that there is a mutually reinforcing relationship between innovation development and high-skilled labor agglomeration within cities.Between cities,however,the relationship between innovation collaboration and the migration of high-skilled labor has not been fully revealed,especially with stronger urban connections and the growing importance of network capital.This study selects 337 prefecture-level and above administrative regions across China,excluding Hong Kong,Macao and Taiwan.It constructs a dual-layer network of‘high-skilled labor migration-intercity innovation’using microdata from the 2015 population sample survey and patent data from 2011 to 2015.The interaction between intercity innovation and high-skilled labor migration networks is empirically examined using Quadratic Assignment Procedure regression and Exponential Random Graph Model,with a focus on two aspects:overall network development and internal network self-organization.The research findings reveal that,in the overall development of both networks,the flow of highly skilled labor promotes the development of intercity innovation networks,which in turn encourage labor migration.With regard to network self-organization,the development of both networks is driven by local interactions between migration and innovation networks.This interaction is not limited to directly connected cities but also enables cities with no direct links to establish innovation or migration relationships through the effect of third-party cities transmitting resources or information.The results confirm a mutual reinforcement between intercity innovation and skilled labor migration,suggesting cities should seize innovation collaboration opportunities,attract skilled labor,and create feedback loops to enhance network capital. 展开更多
关键词 high-skilled labor migration innovation network multilayer network Exponential Random Graph Model China
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The Refinement Algorithm Consideration in Text Clustering Scheme Based on Multilevel Graph 认领 引用
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作者 CHENJian-bin DONGXiang-jun SONGHan-tao 《Wuhan University Journal of Natural Sciences》 EI CAS 2004年第5期671-675,共5页
To construct a high efficient text clustering algorithm the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed. The model is applied to text clustering. The performance of ... To construct a high efficient text clustering algorithm the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed. The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application. The experiment result demonstrated that the multilevel graph text clustering algorithm is available. Key words text clustering - multilevel coarsen graph model - refinement algorithm - high-dimensional clustering CLC number TP301 Foundation item: Supported by the National Natural Science Foundation of China (60173051)Biography: CHEN Jian-bin(1970-), male, Associate professor, Ph. D., research direction: data mining. 展开更多
关键词 text clustering multilevel coarsen graph model refinement algorithm high-dimensional clustering
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Markov Graph Model Computation and Its Application to Intrusion Detection 认领 引用
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作者 曾剑平 郭东辉 《Journal of Donghua University(English Edition)》 EI CAS 2007年第2期272-275,共4页
Markov model is usually selected as the base model of user action in the intrusion detection system (IDS). However, the performance of the IDS depends on the status space of Markov model and it will degrade as the spa... Markov model is usually selected as the base model of user action in the intrusion detection system (IDS). However, the performance of the IDS depends on the status space of Markov model and it will degrade as the space dimension grows. Here, Markov Graph Model (MGM) is proposed to handle this issue. Specification of the model is described, and several methods for probability computation with MGM are also presented. Based on MGM, algorithms for building user model and predicting user action are presented. And the performance of these algorithms such as computing complexity, prediction accuracy, and storage requirement of MGM are analyzed. 展开更多
关键词 Markov Graph Model intrusion detection probability computation
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Study on the Characteristics and Influencing Factors of Multinational Cooperation Network of Digital Technology in the Pharmaceutical Industry from the Perspective of Patents 认领 引用
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作者 Liang Xi Liu Xinrui +1 位作者 Wu Tong Yuan Hongmei 《Asian Journal of Social Pharmacy》 CAS 2026年第1期76-91,共16页
Objective To study the characteristics and influencing factors of the transnational innovation cooperation network of digital technology in the pharmaceutical industry and to promote the construction of a new pattern ... Objective To study the characteristics and influencing factors of the transnational innovation cooperation network of digital technology in the pharmaceutical industry and to promote the construction of a new pattern of global digital pharmaceutical innovation cooperation.Methods An international cooperative innovation network was constructed based on the digital patents of the pharmaceutical industry jointly invented by 79 countries from 2009 to 2020.The overall structural characteristics,status evolution characteristics,spatial evolution characteristics,and influencing factors of the transnational cooperation network of digital technology in the pharmaceutical industry were analyzed using the social network analysis method and the time-exponential random graph model(TERGM).Results and Conclusion The main body of the digital technology transnational cooperation network in the pharmaceutical industry presents diverse forms and has the characteristics of a“small world”.Innovation entities have formed stable cooperative relations,but cooperation needs strengthening.The United States and Germany are strong cooperative countries,while some developing countries such as China,India,and Russia have been weak cooperative countries for a long time.The cooperation network is greatly affected by external environmental factors,showing a three-pronged pattern of Europe,Asia,and North America.The cooperation network tends to form triples and has strong polarization characteristics,which is driven by per capita,national income,population size,technological proximity,and geographical distance. 展开更多
关键词 pharmaceutical industry digital technology cooperative network time-exponential random graph model(TERGM)
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TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models 认领 引用 被引量：2
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作者 Mohamed Ali Rakrouki Nawaf Alharbe +1 位作者 Mashael Khayyat Abeer Aljohani 《Computer Systems Science & Engineering》 SCIE EI 2023年第4期395-408,共14页
Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in r... Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators. 展开更多
关键词 Natural language processing text summarization graph model topic model
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Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration 认领 引用 被引量：1
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作者 Wenpeng Lu Fanqing Meng +4 位作者 Shoujin Wang Guoqiang Zhang Xu Zhang Antai Ouyang Xiaodong Zhang 《Computers, Materials & Continua》 SCIE EI 2019年第7期197-212,共16页
Word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper applications.However,WSD is very challenging due to the problem of kno... Word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper applications.However,WSD is very challenging due to the problem of knowledge bottleneck,i.e.,it is hard to acquire abundant disambiguation knowledge,especially in Chinese.To solve this problem,this paper proposes a graph-based Chinese WSD method with multi-knowledge integration.Particularly,a graph model combining various Chinese and English knowledge resources by word sense mapping is designed.Firstly,the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet.Then,English word similarity is computed based on English word embeddings and knowledge base.Chinese word similarity is evaluated with Chinese word embedding and HowNet,respectively.The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities,which are utilized to construct a disambiguation graph.The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words.Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines. 展开更多
关键词 Word sense disambiguation graph model multi-knowledge integration word similarity
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Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model 认领 引用 被引量：1
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作者 FANG Cheng LIU Jun LEI Zhenming 《China Communications》 SCIE CSCD 2014年第12期13-25,共13页
With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this pap... With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods. 展开更多
关键词 cloud computing massive data graph model web usage mining
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Continuous Multiplicative Attribute Graph Model 认领 引用 被引量：1
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作者 黄嘉烜 金小刚 《Journal of Shanghai Jiaotong university(Science)》 EI 2017年第1期87-91,共5页
Network modeling is an important approach in many fields in analyzing complex systems. Recently new series of methods have emerged, by using Kronecker product and similar tools to model real systems. One of such appro... Network modeling is an important approach in many fields in analyzing complex systems. Recently new series of methods have emerged, by using Kronecker product and similar tools to model real systems. One of such approaches is the multiplicative attribute graph(MAG) model, which generates networks based on category attributes of nodes. In this paper we try to extend this model into a continuous one, give an overview of its properties, and discuss some special cases related to real-world networks, as well as the influence of attribute distribution and affinity function respectively. 展开更多
关键词 multiplicative attribute graph model social network continuous attribute TP 181 A
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A Grid-based Graph Data Model for Pedestrian Route Analysis in a Micro-spatial Environment 认领 引用
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作者 Yi-Quan Song Lei Niu +1 位作者 Long He Rui Wang 《International Journal of Automation and computing》 CSCD 2016年第3期296-304,共9页
Due to limitations in geometric representation and semantic description, the current pedestrian route analysis models are inadequate. To express the geometry of geographic entities in a micro-spatial environment accur... Due to limitations in geometric representation and semantic description, the current pedestrian route analysis models are inadequate. To express the geometry of geographic entities in a micro-spatial environment accurately, the concept of a grid is presented, and grid-based methods for modeling geospatial objects are described. The semantic constitution of a building environment and the methods for modeling rooms, corridors, and staircases with grid objects are described. Based on the topology relationship between grid objects, a grid-based graph for a building environment is presented, and the corresponding route algorithm for pedestrians is proposed. The main advantages of the graph model proposed in this paper are as follows： 1） consideration of both semantic and geometric information, 2） consideration of the need for accurate geometric representation of the micro-spatial environment and the efficiency of pedestrian route analysis, 3） applicability of the graph model to route analysis in both static and dynamic environments, and 4） ability of the multi-hierarchical route analysis to integrate the multiple levels of pedestrian decision characteristics, from the high to the low, to determine the optimal path. 展开更多
关键词 Graph data model route analysis pedestrian micro-spatiM environment building.
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User Churn Prediction Hierarchical Model Based on Graph Attention Convolutional Neural Networks 认领 引用
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作者 Mei Miao Tang Miao Zhou Long 《China Communications》 SCIE CSCD 2024年第7期169-185,共17页
The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications ... The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses. 展开更多
关键词 cloud-edge cooperative framework GAT-CNN self-attention and graph convolution models subscriber churn prediction
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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems 认领 引用
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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
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Document Clustering Using Graph Based Fuzzy Association Rule Generation 认领 引用
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作者 P.Perumal 《Computer Systems Science & Engineering》 SCIE EI 2022年第10期203-218,共16页
With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is increased.Here,document summarization that is extracting the essential task with appropriate inform... With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is increased.Here,document summarization that is extracting the essential task with appropriate information,removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task.In this research,a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation(gFAR).Initially,the graph model is used to map the relationship among the data(multi-source)followed by the establishment of document clustering with the generation of association rule using the fuzzy concept.This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy.This framework is provided in an interpretable way for document clustering.It iteratively reduces the error rate during relationship mapping among the data(clusters)with the assistance of weighted document content.Also,this model represents the significance of data features with class discrimination.It is also helpful in measuring the significance of the features during the data clustering process.The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns(RRP),ROUGE score,and Discrimination Information Measure(DMI)respectively.Here,DailyMail and DUC 2004 dataset is used to extract the empirical results.The proposed gFAR model gives better trade-off while compared with various prevailing approaches. 展开更多
关键词 Document clustering text summarization fuzzy model association rule generation graph model relevance mapping feature patterns
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Graph-based Lexicalized Reordering Models for Statistical Machine Translation 认领 引用
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作者 SU Jinsong LIU Yang +1 位作者 LIU Qun DONG Huailin 《China Communications》 SCIE CSCD 2014年第5期71-82,共12页
Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word a... Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word aligned bilingual corpus,while ignoring the effect of the number of adjacent bilingual phrases.In this paper,we propose a method to take the number of adjacent phrases into account for better estimation of reordering models.Instead of just checking whether there is one phrase adjacent to a given phrase,our method firstly uses a compact structure named reordering graph to represent all phrase segmentations of a parallel sentence,then the effect of the adjacent phrase number can be quantified in a forward-backward fashion,and finally incorporated into the estimation of reordering models.Experimental results on the NIST Chinese-English and WMT French-Spanish data sets show that our approach significantly outperforms the baseline method. 展开更多
关键词 natural language processing statistical machine translation lexicalized reordering model reordering graph
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Multi-Polar Evolution of Global Inventive Talent Flow Network-An Endogenous Migration Model and Empirical Analysis 认领 引用
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作者 Zheng Jianghuai Sun Dongqing +1 位作者 Dai Wei Shi Lei 《中国经济学人（中英文）》 2025年第4期80-100,共21页
The global clustering of inventive talent shapes innovation capacity and drives economic growth.For China,this process is especially crucial in sustaining its development momentum.This paper draws on data from the EPO... The global clustering of inventive talent shapes innovation capacity and drives economic growth.For China,this process is especially crucial in sustaining its development momentum.This paper draws on data from the EPO Worldwide Patent Statistical Database(PATSTAT)to extract global inventive talent mobility information and analyzes the spatial structural evolution of the global inventive talent flow network.The study finds that this network is undergoing a multi-polar transformation,characterized by the rising importance of a few central countries-such as the United States,Germany,and China-and the increasing marginalization of many peripheral countries.In response to this typical phenomenon,the paper constructs an endogenous migration model and conducts empirical testing using the Temporal Exponential Random Graph Model(TERGM).The results reveal several endogenous mechanisms driving global inventive talent flows,including reciprocity,path dependence,convergence effects,transitivity,and cyclic structures,all of which contribute to the network’s multi-polar trend.In addition,differences in regional industrial structures significantly influence talent mobility choices and are a decisive factor in the formation of poles within the multi-polar landscape.Based on these findings,it is suggested that efforts be made to foster two-way channels for talent exchange between China and other global innovation hubs,in order to enhance international collaboration and knowledge flow.We should aim to reduce the migration costs and institutional barriers faced by R&D personnel,thereby encouraging greater mobility of high-skilled talent.Furthermore,the government is advised to strategically leverage regional strengths in high-tech industries as a lever to capture competitive advantages in emerging technologies and products,ultimately strengthening the country’s position in the global innovation landscape. 展开更多
关键词 Inventive talent flow network multipolarity spatial structural evolution regional industrial structure disparities temporal exponential random graph model(TERGM)
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