Network Mining and Propagation Dynamics Analysis

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Network Mining and Propagation Dynamics Analysis

75.4K

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Editorial

24 January 2023

Editorial: Network mining and propagation dynamics analysis

Xinyi Wang

Yuexia Zhang

Xuzhen Zhu

Fei Xiong

, 1 more and

Shirui Pan

2,312 views

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Editors

Xuzhen Zhu

Beijing University of Posts and Telecommunications (BUPT)

Wei Wang

Chongqing Medical University

Shirui Pan

Monash University

Fei Xiong

Beijing Jiaotong University

Impact

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Many phenomena in the fields of computer science, biology, sociology, and economics can be described as transmission dynamics on complex networks, and transmission dynamics mainly includes information transmission, disease transmission, and computer virus transmission. In many real propagation phenomena, we often want to know their propagation mechanism and law on complex networks, as well as their prediction methods and control means. Clarifying the above problems allows people to have a clear and comprehensive understanding of the evolution mechanism, propagation process, and steady-state of real phenomena. At the same time, it also provides some necessary theoretical support for predicting and controlling real systems. In recent years, many concepts and methods of statistical physics have also been successfully used in the modeling and calculation of complex networks, such as statistical mechanics, self-organization theory, critical and phase transition theory, seepage theory, and so on. In complex networks, seepage can simulate and describe the growth and evolution characteristics of many natural and social systems. Based on the classical network seepage, people have carried out a lot of research on the explosive seepage in the process of network growth in recent years. Using the seepage method, researchers have drawn many conclusions and brought new ideas in the research directions of network propagation and cascade failure. By using these theories, many researchers have quantitatively analyzed the influence of many factors on transmission path and transmission mechanism, and then discussed the effect of the control strategy.

With the rapid development of network science, the study of link prediction is closely related to the structure and evolution of networks. Meanwhile, such research can also help us understand the evolution mechanism of complex networks theoretically, so as to better help us deduce the propagation dynamics of complex networks. At present, the application of large-scale real data to complex networks still lacks in-depth analysis and research. The explosive growth of data in the network has brought new challenges to the prediction of the nodes relationship and propagation process. In order to precisely predict future data, it is necessary to design efficient and accurate models, and one of the solutions is to optimize network nodes, which can improve the efficiency of data transmission. Furthermore, how to predict the possibility of links on nodes and how to truly simulate the propagation mechanism between nodes are also urgent problems to be dealt with.

The goal of this Special Issue in Frontiers in Physics is to welcome the contribution of complex networks, a rapidly developing research field. We encourage articles that use multidisciplinary methods for complex network data mining, such as machine learning, information theory, applied mathematics, and computational statistical physics. Potential topics include but are not limited to the following:
? Trend analysis of social network information dissemination
? Analysis of the spread trend of infectious diseases
? Analysis of computer virus transmission process
? Link prediction on social networks
? Behavior analysis on social networks
? Network state prediction
? Pattern recognition of behaviors
? Personalized recommender systems

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Editors

Xuzhen Zhu

Beijing University of Posts and Telecommunications (BUPT)

Wei Wang

Chongqing Medical University

Shirui Pan

Monash University

Fei Xiong

Beijing Jiaotong University

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Social Physics

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:null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1019118","pubDate":"2022-10-04","score":5.175552274715539,"title":"Social contagion influenced by active-passive psychology of college students","topics":["Phase Transition","Complex Network","Crossover phenomena","Behavioral propagation","Active-passive psychology"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1019118/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bc9","abstract":"Dialog systems have attracted attention as they are promising in many intelligent applications. Generating fluent and informative responses is of critical importance for dialog systems. Some recent studies introduce documents as extra knowledge to improve the performance of dialog generation. However, it is hard to understand the unstructured document and extract crucial information related to dialog history and current utterance. This leads to uninformative and inflexible responses in existing studies. To address this issue, we propose a generative model of a neural network with an attention mechanism for document-grounded multi-turn dialog. This model encodes the context of utterances that contains the given document, dialog history, and the last utterance into distributed representations via a triple-channel. Then, it introduces a hierarchical attention interaction between dialog contexts and previously generated utterances into the decoder for generating an appropriate response. We compare our model with various baselines on dataset CMU_DoG in terms of the evaluation criteria. The experimental results demonstrate the state-of-the-art performance of our model as compared to previous studies. Furthermore, the results of ablation experiments show the effectiveness of the hierarchical attention interaction and the triple channel for encoding. We also conduct human judgment to evaluate the informativeness of responses and the consistency of responses with dialog history.","htmlAbstract":"\u003cp\u003eDialog systems have attracted attention as they are promising in many intelligent applications. Generating fluent and informative responses is of critical importance for dialog systems. Some recent studies introduce documents as extra knowledge to improve the performance of dialog generation. However, it is hard to understand the unstructured document and extract crucial information related to dialog history and current utterance. This leads to uninformative and inflexible responses in existing studies. To address this issue, we propose a generative model of a neural network with an attention mechanism for document-grounded multi-turn dialog. This model encodes the context of utterances that contains the given document, dialog history, and the last utterance into distributed representations \u003cem\u003evia\u003c/em\u003e a triple-channel. Then, it introduces a hierarchical attention interaction between dialog contexts and previously generated utterances into the decoder for generating an appropriate response. We compare our model with various baselines on dataset CMU_DoG in terms of the evaluation criteria. The experimental results demonstrate the state-of-the-art performance of our model as compared to previous studies. Furthermore, the results of ablation experiments show the effectiveness of the hierarchical attention interaction and the triple channel for encoding. We also conduct human judgment to evaluate the informativeness of responses and the consistency of responses with dialog history.\u003c/p\u003e","authors":[{"fullName":"Yuanyuan Cai","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1959168/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1959168/overview","affiliation":{"name":"National Engineering Research Centre for Agri-Product Quality Traceability","address":null},"affiliations":[{"name":"National Engineering Research Centre for Agri-Product Quality Traceability","address":null},{"name":"School of E-Business and Logistics","address":null}],"nessieId":"481036981897"},{"fullName":"Min Zuo","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/403764/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/403764/overview","affiliation":{"name":"National Engineering Research Centre for Agri-Product Quality Traceability","address":null},"affiliations":[{"name":"National Engineering Research Centre for Agri-Product Quality Traceability","address":null},{"name":"School of E-Business and Logistics","address":null}],"nessieId":"953483328734"},{"fullName":"Haitao Xiong","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2010050/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2010050/overview","affiliation":{"name":"National Engineering Research Centre for Agri-Product Quality Traceability","address":null},"affiliations":[{"name":"National Engineering Research Centre for Agri-Product Quality Traceability","address":null},{"name":"School of International Economics and Management","address":null}],"nessieId":"25770390267"}],"dates":{"acceptedDate":"2022-09-14","recentDate":"2022-10-03"},"doi":"10.3389/fphy.2022.1019969","frontiersExtra":{"articleType":"Original Research","impact":{"citations":1,"crossrefCitations":0,"downloads":719,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":2726},"isPartOfResearchTopic":true,"isPublished":true,"section":"Social Physics"},"guid":1019969,"images":[{"height":94,"url":"https://www.frontiersin.org/files/myhome article library/1019969/1019969_Thumb_400.jpg","width":400,"caption":null},{"height":753,"url":"https://www.frontiersin.org/files/Articles/1019969/fphy-10-1019969-HTML/image_m/fphy-10-1019969-g001.jpg","width":941,"caption":"Framework of the model. The left is the context encoder with the triple channel. The right is the decoder interacted with contextual encoding for response generation."}],"journal":{"guid":616,"name":"Frontiers in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1019969","pubDate":"2022-10-03","score":4.05459864391942,"title":"Modeling hierarchical attention interaction between contexts and triple-channel encoding networks for document-grounded dialog generation","topics":["transformer","Context-aware","encoder–decoder framework","hierarchical attention interaction","semantic feature encoding","document-grounded conversation generation"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1019969/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bcb","abstract":"In order to improve the city network mining method, the inter-cities’ connection strength, structure and density, and distribution patterns of city network in the Yangtze River Economic Belt of China have been empirically analyzed through the combined application of SNA method, “Dual-direction time distance” modified gravity model and ArcGIS geographic visualization method. The results show that the modified gravity model can better reveal the interaction differences between cities and reflect the current and potential economic, population and resource relations among cities. The city network density of this area has positively close relationship with the regional economic development level. The average value of degree centrality in the basin is high, but the difference between cities is obvious. The “agglomeration effect” of the central cities is significant, and the urban connections have an obvious cluster structure, showing an “M” shaped spatial distribution along the Yangtze River; The inner interaction strength of city network subgroups is high, but the connection between subgroups is low. The law of “downstream \u003e midstream \u003e upstream” also appears on the closeness centrality and betweenness centrality. In the future, it is essential to improve the integration and multi-level connections of urban agglomeration in the river basin and form a development pattern of “downstream driving - midstream transition - upstream connection”; strengthen the functions and connections of central and subcentral cities.","htmlAbstract":"\u003cp\u003eIn order to improve the city network mining method, the inter-cities\u0026#x2019; connection strength, structure and density, and distribution patterns of city network in the Yangtze River Economic Belt of China have been empirically analyzed through the combined application of SNA method, \u0026#x201c;Dual-direction time distance\u0026#x201d; modified gravity model and ArcGIS geographic visualization method. The results show that the modified gravity model can better reveal the interaction differences between cities and reflect the current and potential economic, population and resource relations among cities. The city network density of this area has positively close relationship with the regional economic development level. The average value of degree centrality in the basin is high, but the difference between cities is obvious. The \u0026#x201c;agglomeration effect\u0026#x201d; of the central cities is significant, and the urban connections have an obvious cluster structure, showing an \u0026#x201c;M\u0026#x201d; shaped spatial distribution along the Yangtze River; The inner interaction strength of city network subgroups is high, but the connection between subgroups is low. The law of \u0026#x201c;downstream \u0026gt; midstream \u0026gt; upstream\u0026#x201d; also appears on the closeness centrality and betweenness centrality. In the future, it is essential to improve the integration and multi-level connections of urban agglomeration in the river basin and form a development pattern of \u0026#x201c;downstream driving - midstream transition - upstream connection\u0026#x201d;; strengthen the functions and connections of central and subcentral cities.\u003c/p\u003e","authors":[{"fullName":"Duo Chai","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1950125/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1950125/overview","affiliation":{"name":"School of Government","address":null},"affiliations":[{"name":"School of Government","address":null}],"nessieId":"833224316188"},{"fullName":"Jiaze Du","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Government","address":null},"affiliations":[{"name":"School of Government","address":null}],"nessieId":null},{"fullName":"Zongqi Yu","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Government","address":null},"affiliations":[{"name":"School of Government","address":null}],"nessieId":null},{"fullName":"Dong Zhang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Public Administration and Policy","address":null},"affiliations":[{"name":"School of Public Administration and Policy","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2022-09-12","recentDate":"2022-09-30"},"doi":"10.3389/fphy.2022.1018993","frontiersExtra":{"articleType":"Original Research","impact":{"citations":18,"crossrefCitations":0,"downloads":1233,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":5150},"isPartOfResearchTopic":true,"isPublished":true,"section":"Social Physics"},"guid":1018993,"images":[{"height":346,"url":"https://www.frontiersin.org/files/myhome article library/1018993/1018993_Thumb_400.jpg","width":400,"caption":null},{"height":518,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g001.jpg","width":1065,"caption":"Resource and environment connection logic of basin cities."},{"height":533,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g002.jpg","width":699,"caption":"The logical relationship between economy, population, transportation and city network."},{"height":651,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g003.jpg","width":948,"caption":"Research scope and Spatial distribution of relevant provinces."},{"height":905,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g004.jpg","width":809,"caption":"City contact strength max-value distribution in the research area."},{"height":912,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g005.jpg","width":809,"caption":"City contact strength min-value distribution in the research area."},{"height":572,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g006.jpg","width":809,"caption":"Out-degree centrality value distribution in the research area."},{"height":572,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g007.jpg","width":809,"caption":"Out-closeness centrality value distribution in the research area."},{"height":572,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g008.jpg","width":809,"caption":"In-degree centrality value distribution in the research area."},{"height":572,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g009.jpg","width":809,"caption":"In-closeness centrality value distribution in the research area."},{"height":573,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g010.jpg","width":809,"caption":"Betweenness centrality value distribution in the research area."},{"height":567,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g011.jpg","width":892,"caption":"City networks structure of the Yangtze River Basin area."},{"height":574,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g012.jpg","width":809,"caption":"High network density value distribution shape in the Yangtze River Basin area."},{"height":919,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g013.jpg","width":1065,"caption":"Cohesive subgroups in the research area."},{"height":754,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g014.jpg","width":1065,"caption":"Current Situation of cohesive subgroups in the research area."},{"height":558,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g015.jpg","width":809,"caption":"Path diagram of inter-city official transfer in the research area."},{"height":574,"url":"https://www.frontiersin.org/files/Articles/1018993/fphy-10-1018993-HTML/image_m/fphy-10-1018993-g016.jpg","width":809,"caption":"Current situation of the amount of primary city officers transferred between cities in the research area."}],"journal":{"guid":616,"name":"Frontiers in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1018993","pubDate":"2022-09-30","score":23.752734033245677,"title":"City network mining in china’s yangtze river economic belt based on “two-way time distance” modified gravity model and social network analysis","topics":["social network analysis","Gravity model","City network","Yangtze River Economic Belt","dual-direction time distance"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1018993/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bd2","abstract":"Emergency cooperative social networks (ECSNs) play a very important role in emergency management for magnitude emergencies in China recently. Based on the data set of cooperative fight against COVID-19 of the Beijing-Tianjin-Hebei region in China, using social network analysis (SNA) and asymmetric evolutionary game model, this study finds that the asymmetry between regions is comprehensively determined by resource endowment, administrative level, geographical distance, regional vulnerability, political pressure and other factors; vertical control is still the main operating mechanism of ECSNs; network derivation is caused by the superposition of multiple factors, of which political factors are very important, and asymmetry may become an obstacle.","htmlAbstract":"\u003cp\u003eEmergency cooperative social networks (ECSNs) play a very important role in emergency management for magnitude emergencies in China recently. Based on the data set of cooperative fight against COVID-19 of the Beijing-Tianjin-Hebei region in China, using social network analysis (SNA) and asymmetric evolutionary game model, this study finds that the asymmetry between regions is comprehensively determined by resource endowment, administrative level, geographical distance, regional vulnerability, political pressure and other factors; vertical control is still the main operating mechanism of ECSNs; network derivation is caused by the superposition of multiple factors, of which political factors are very important, and asymmetry may become an obstacle.\u003c/p\u003e","authors":[{"fullName":"Rui Nan","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Law and Humanities","address":null},"affiliations":[{"name":"School of Law and Humanities","address":null}],"nessieId":null},{"fullName":"Jingjie Wang","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1857405/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1857405/overview","affiliation":{"name":"School of Law and Humanities","address":null},"affiliations":[{"name":"School of Law and Humanities","address":null}],"nessieId":"644245749180"}],"dates":{"acceptedDate":"2022-08-11","recentDate":"2022-09-27"},"doi":"10.3389/fphy.2022.986605","frontiersExtra":{"articleType":"Original Research","impact":{"citations":5,"crossrefCitations":0,"downloads":733,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":2869},"isPartOfResearchTopic":true,"isPublished":true,"section":"Social Physics"},"guid":986605,"images":[{"height":328,"url":"https://www.frontiersin.org/files/myhome article library/986605/986605_Thumb_400.jpg","width":400,"caption":null},{"height":490,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g001.jpg","width":754,"caption":"map of the ECSNs for COVID-19 in the B-T-H region."},{"height":485,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g002.jpg","width":692,"caption":"Clique analysis of the ECSNs for COVID-19 in the B-T-H region."},{"height":341,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g003.jpg","width":505,"caption":"Game phase diagram."},{"height":409,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g004.jpg","width":505,"caption":"The influence of asymmetric (μ) on the evolution of ECSNs."},{"height":414,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g005.jpg","width":505,"caption":"The effect of extra cost (β) on the evolution of ECSNs when both parties cooperate negatively."},{"height":415,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g006.jpg","width":505,"caption":"The effect of extra cost (θ) on the evolution of ECSNs when one party cooperates negatively."},{"height":393,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g007.jpg","width":505,"caption":"The effect of basic punishment F on the evolution of ECSNs."},{"height":406,"url":"https://www.frontiersin.org/files/Articles/986605/fphy-10-986605-HTML/image_m/fphy-10-986605-g008.jpg","width":498,"caption":"The effect of penalty adjustment (λ) on the evolution of ECSNs."}],"journal":{"guid":616,"name":"Frontiers in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.986605","pubDate":"2022-09-27","score":8.210411198600081,"title":"Asymmetric evolutionary game analysis of emergency cooperative social networks for magnitude emergencies: Evidence from the Beijing-Tianjin-Hebei region in China","topics":["asymmetry","Game theory","Social network analysis (SNA)","Emergency Cooperative Social Networks (ECSNs)","Magnitude Emergencies"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.986605/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bd0","abstract":"China has become the world’s largest market for the production and sales of new energy vehicles. In the Internet era, online review data is becoming more and more important, and it is a great challenge for new energy vehicle manufacturers and consumers to quickly obtain and find out the effective information in the review data. In view of the above understanding, this study uses the Bert-wwm-ext model structure, data mining, and deep learning to study the new energy vehicle selection, and also analyzes the positioning of domestic and foreign new energy vehicle brands and their brand development from the perspective of complex networks. The research results found that: 1) Consumers pay more and more attention to the quality of new energy vehicles. 2) The comparative analysis of BEV and PHEV reveals that consumers’ evaluation of both types of vehicles is roughly comparable, but the difference in satisfaction with the endurance of both types of vehicles is very obvious. 3) Most of the brands of new energy vehicles are concentrated in the price range of RMB80,000 to RMB350,000, and within this range, consumers’ evaluation is positively correlated with the price of the vehicle. Among the new energy vehicle brands over RMB350,000, consumer evaluation does not rise with the price of the vehicle. 4) The head effect of Chinese brands is significant, Foreign brands have formed strong brands with high brand premiums.","htmlAbstract":"\u003cp\u003eChina has become the world\u0026#x2019;s largest market for the production and sales of new energy vehicles. In the Internet era, online review data is becoming more and more important, and it is a great challenge for new energy vehicle manufacturers and consumers to quickly obtain and find out the effective information in the review data. In view of the above understanding, this study uses the Bert-wwm-ext model structure, data mining, and deep learning to study the new energy vehicle selection, and also analyzes the positioning of domestic and foreign new energy vehicle brands and their brand development from the perspective of complex networks. The research results found that: 1) Consumers pay more and more attention to the quality of new energy vehicles. 2) The comparative analysis of BEV and PHEV reveals that consumers\u0026#x2019; evaluation of both types of vehicles is roughly comparable, but the difference in satisfaction with the endurance of both types of vehicles is very obvious. 3) Most of the brands of new energy vehicles are concentrated in the price range of RMB80,000 to RMB350,000, and within this range, consumers\u0026#x2019; evaluation is positively correlated with the price of the vehicle. Among the new energy vehicle brands over RMB350,000, consumer evaluation does not rise with the price of the vehicle. 4) The head effect of Chinese brands is significant, Foreign brands have formed strong brands with high brand premiums.\u003c/p\u003e","authors":[{"fullName":"Hui Liu","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1800917/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1800917/overview","affiliation":{"name":"Faculty of Applied Economics","address":null},"affiliations":[{"name":"Faculty of Applied Economics","address":null},{"name":"Center for Brand Leadership","address":null}],"nessieId":"541166537934"},{"fullName":"Lei Feng","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1951330/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1951330/overview","affiliation":{"name":"Center for Brand Leadership","address":null},"affiliations":[{"name":"Center for Brand Leadership","address":null},{"name":"Faculty of Economics, University of Chinese Academy of Social Sciences","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2022-09-09","recentDate":"2022-09-23"},"doi":"10.3389/fphy.2022.1015103","frontiersExtra":{"articleType":"Original Research","impact":{"citations":7,"crossrefCitations":0,"downloads":1075,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":5661},"isPartOfResearchTopic":true,"isPublished":true,"section":"Social Physics"},"guid":1015103,"images":[{"height":165,"url":"https://www.frontiersin.org/files/myhome article library/1015103/1015103_Thumb_400.jpg","width":400,"caption":null},{"height":352,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g001.jpg","width":858,"caption":"Bert-wwm-ext model."},{"height":462,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g002.jpg","width":429,"caption":"Transformer encoder."},{"height":129,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g003.jpg","width":934,"caption":"Masked language model of Bert."},{"height":128,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g004.jpg","width":941,"caption":"Masked language model of BERT-wmm-ext."},{"height":156,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g005.jpg","width":941,"caption":"Next sentence prediction task."},{"height":786,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g006.jpg","width":699,"caption":"“ Reasons for choosing this car” Word Cloud."},{"height":426,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g007.jpg","width":809,"caption":"2014–2022 emotional trend chart."},{"height":419,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g008.jpg","width":809,"caption":"2014–2022 emotional trend chart 2"},{"height":425,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g009.jpg","width":809,"caption":"2014–2022 emotional trend of BEV"},{"height":425,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g010.jpg","width":809,"caption":"2014–2022 emotional trend of PHEVs."},{"height":493,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g011.jpg","width":484,"caption":"A comparative radar chart of BEV and PHEV"},{"height":1236,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g012.jpg","width":1059,"caption":"The eight-dimensional emotional distribution map of different brands of cars."},{"height":621,"url":"https://www.frontiersin.org/files/Articles/1015103/fphy-10-1015103-HTML/image_m/fphy-10-1015103-g013.jpg","width":1059,"caption":"Distribution map of the average sentiment score of different brands of cars."}],"journal":{"guid":616,"name":"Frontiers in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1015103","pubDate":"2022-09-23","score":13.337871828521248,"title":"The study of new energy vehicle choice in China from the perspective of complex neural network","topics":["Data Mining","complex networks","Natural Language Processing","deep learning","New energy vehicles"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.1015103/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bca","abstract":"The Stock Market is a typical complex network composed of investors, stocks, and market information. The abnormal fluctuation of the Stock Market has a strong effect on the economy of a country and even that of the world. Fueled by the herd effect of the increasingly abundant social media, Internet rumors, as an important source of market information and an exogenous financial risk, can lead to the collapse of investor confidence and the further propagation of financial risks, which can damage the financial system and even lead to social unrest. With additional availability of computing techniques, we attempt to uncover the media information effects in the stock market and seek to provide researchers with 1) a theoretical reference for a comprehensive understanding of such a complex network, 2) accurate prediction of future data, and 3) design of efficient and reliable risk intervention models. Based on the data of China’s Stock Market, this study uses machine learning to investigate social media rumors to reveal the interplay of social media rumors and stock market volatility. In this work, we find patterns from social media rumors from financial forums using machine learning, quantify social media rumors based on statistics, and analyze the mechanism of propagation and influence of social media rumors on stock market volatility using econometric models. The empirical results show that rumors play an important information transmission effect on stock market volatility and the constructed Internet Financial Forum Rumor Index is helpful to sense the potential impact of rumors, i.e., a significant lagged negative effect. These findings are of guidance for the optimization of the information environment, and can serve to promote the healthy and stable development of the stock market.","htmlAbstract":"\u003cp\u003eThe Stock Market is a typical complex network composed of investors, stocks, and market information. The abnormal fluctuation of the Stock Market has a strong effect on the economy of a country and even that of the world. Fueled by the herd effect of the increasingly abundant social media, Internet rumors, as an important source of market information and an exogenous financial risk, can lead to the collapse of investor confidence and the further propagation of financial risks, which can damage the financial system and even lead to social unrest. With additional availability of computing techniques, we attempt to uncover the media information effects in the stock market and seek to provide researchers with 1) a theoretical reference for a comprehensive understanding of such a complex network, 2) accurate prediction of future data, and 3) design of efficient and reliable risk intervention models. Based on the data of China\u0026#x2019;s Stock Market, this study uses machine learning to investigate social media rumors to reveal the interplay of social media rumors and stock market volatility. In this work, we find patterns from social media rumors from financial forums using machine learning, quantify social media rumors based on statistics, and analyze the mechanism of propagation and influence of social media rumors on stock market volatility using econometric models. The empirical results show that rumors play an important information transmission effect on stock market volatility and the constructed Internet Financial Forum Rumor Index is helpful to sense the potential impact of rumors, i.e., a significant lagged negative effect. These findings are of guidance for the optimization of the information environment, and can serve to promote the healthy and stable development of the stock market.\u003c/p\u003e","authors":[{"fullName":"Hua Zhang","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1780943/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1780943/overview","affiliation":{"name":"School of Economics and Management","address":null},"affiliations":[{"name":"School of Economics and Management","address":null}],"nessieId":"163209247555"},{"fullName":"Yuanzhu Chen","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1956154/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1956154/overview","affiliation":{"name":"School of Computing","address":null},"affiliations":[{"name":"School of Computing","address":null}],"nessieId":"712965226550"},{"fullName":"Wei Rong","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1956808/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1956808/overview","affiliation":{"name":"School of Management Science and Engineering","address":null},"affiliations":[{"name":"School of Management Science and Engineering","address":null}],"nessieId":"944893442438"},{"fullName":"Jun Wang","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1534568/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1534568/overview","affiliation":{"name":"School of Computing and Artificial Intelligence","address":null},"affiliations":[{"name":"School of Computing and Artificial Intelligence","address":null}],"nessieId":"111669808932"},{"fullName":"Jinghua Tan","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1755144/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1755144/overview","affiliation":{"name":"School of Computing and Artificial Intelligence","address":null},"affiliations":[{"name":"School of Computing and Artificial Intelligence","address":null}],"nessieId":"128849678444"}],"dates":{"acceptedDate":"2022-07-28","recentDate":"2022-08-30"},"doi":"10.3389/fphy.2022.987799","frontiersExtra":{"articleType":"Original Research","impact":{"citations":19,"crossrefCitations":0,"downloads":2703,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":19635},"isPartOfResearchTopic":true,"isPublished":true,"section":"Social Physics"},"guid":987799,"images":[{"height":32,"url":"https://www.frontiersin.org/files/myhome article library/987799/987799_Thumb_400.jpg","width":400,"caption":null},{"height":442,"url":"https://www.frontiersin.org/files/Articles/987799/fphy-10-987799-HTML/image_m/fphy-10-987799-g001.jpg","width":692,"caption":"Rumor spreading behavior diagram."},{"height":295,"url":"https://www.frontiersin.org/files/Articles/987799/fphy-10-987799-HTML/image_m/fphy-10-987799-g002.jpg","width":1065,"caption":"Distribution of trading venues, sectors and industry."},{"height":271,"url":"https://www.frontiersin.org/files/Articles/987799/fphy-10-987799-HTML/image_m/fphy-10-987799-g003.jpg","width":1065,"caption":"Distribution of Time. The vertical coordinate is the number of rumors, and the horizontal coordinate is the different time distribution (A): 1 day; (B) 1 week; (C) observation period)."}],"journal":{"guid":616,"name":"Frontiers in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.987799","pubDate":"2022-08-30","score":40.988626421696644,"title":"Effect of social media rumors on stock market volatility: A case of data mining in China","topics":["Information Dissemination","machine learning","Complex Network","Stock Market","Social Media Rumors"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.987799/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bc7","abstract":"In the field of complex network research, complex network information transmission models based on infectious disease models are often used to study the mechanism of information transmission. This is helpful for the prediction of information transmission trends and the formulation of control strategies. However, the classification of node types in traditional information transmission models is too simple and cannot reflect the characteristics of each node. To solve the above problems, this study proposes a layered SITR complex network information transmission model. The model is layered according to the influence of nodes, and rational propagator nodes are added to optimize it. The propagation threshold of the model is deduced theoretically and the stability of the model is proved. To reduce the dissemination scale of the network’s public opinion information, an optimal control strategy is proposed based on the Pontryagin maximum principle to optimize the information dissemination process. Finally, combined with real events from social network platform, the simulation results show that the layered SITR model can describe the process of network information dissemination more accurately, and the optimal control strategy can effectively reduce the dissemination scale of the network’s public opinion information.","htmlAbstract":"\u003cp\u003eIn the field of complex network research, complex network information transmission models based on infectious disease models are often used to study the mechanism of information transmission. This is helpful for the prediction of information transmission trends and the formulation of control strategies. However, the classification of node types in traditional information transmission models is too simple and cannot reflect the characteristics of each node. To solve the above problems, this study proposes a layered SITR complex network information transmission model. The model is layered according to the influence of nodes, and rational propagator nodes are added to optimize it. The propagation threshold of the model is deduced theoretically and the stability of the model is proved. To reduce the dissemination scale of the network\u0026#x2019;s public opinion information, an optimal control strategy is proposed based on the Pontryagin maximum principle to optimize the information dissemination process. Finally, combined with real events from social network platform, the simulation results show that the layered SITR model can describe the process of network information dissemination more accurately, and the optimal control strategy can effectively reduce the dissemination scale of the network\u0026#x2019;s public opinion information.\u003c/p\u003e","authors":[{"fullName":"Dawei Pan","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1828215/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1828215/overview","affiliation":{"name":"School of Information and Communication Engineering","address":null},"affiliations":[{"name":"School of Information and Communication Engineering","address":null}],"nessieId":"85899999541"},{"fullName":"Yuexia Zhang","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1895046/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1895046/overview","affiliation":{"name":"Key Laboratory of Modern Measurement \u0026 Control Technology","address":null},"affiliations":[{"name":"Key Laboratory of Modern Measurement \u0026 Control Technology","address":null},{"name":"State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","address":null},{"name":"Key Laboratory of Information and Communication Systems","address":null}],"nessieId":"249108762431"}],"dates":{"acceptedDate":"2022-07-22","recentDate":"2022-08-23"},"doi":"10.3389/fphy.2022.985517","frontiersExtra":{"articleType":"Original Research","impact":{"citations":11,"crossrefCitations":0,"downloads":843,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":3193},"isPartOfResearchTopic":true,"isPublished":true,"section":"Social Physics"},"guid":985517,"images":[{"height":329,"url":"https://www.frontiersin.org/files/myhome article library/985517/985517_Thumb_400.jpg","width":400,"caption":null},{"height":301,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g001.jpg","width":512,"caption":"The information dissemination process of L-SITR model."},{"height":409,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g002.jpg","width":512,"caption":"Variation in the numbers of I when R0\u003c1."},{"height":409,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g003.jpg","width":512,"caption":"Variations in the numbers of S,T,R when R0\u003c1."},{"height":312,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g004.jpg","width":512,"caption":"Propagation threshold validation for R0\u003c1."},{"height":409,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g005.jpg","width":512,"caption":"Variations in the numbers of I when R0\u003e1."},{"height":409,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g006.jpg","width":512,"caption":"Variations in the numbers of S,T,R when R0\u003e1."},{"height":312,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g007.jpg","width":512,"caption":"Propagation threshold validation for R0\u003e1."},{"height":409,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g008.jpg","width":512,"caption":"Simulation of an actual case."},{"height":414,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g009.jpg","width":512,"caption":"Variation in u0 subject to the control of an uninformed person."},{"height":402,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g010.jpg","width":512,"caption":"Comparison of the changes in the number of nodes without control and subject to the control of an uninformed person."},{"height":421,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g011.jpg","width":512,"caption":"u1 and u2 subject to propagator control."},{"height":402,"url":"https://www.frontiersin.org/files/Articles/985517/fphy-10-985517-HTML/image_m/fphy-10-985517-g012.jpg","width":512,"caption":"Comparison of the changes in the number of nodes without and with control of the propagator."}],"journal":{"guid":616,"name":"Frontiers in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.985517","pubDate":"2022-08-23","score":14.566874453193247,"title":"Analysis of information propagation and control of a layered SITR model in complex networks","topics":["complex networks","optimal control","stability analysis","Propagation dynamics","Infectious disease model"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.985517/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bd3","abstract":"Previous studies have demonstrated that short-term exposure to ambient air pollution was associated with hospital admissions for cardiovascular diseases, but the evidence of its effects on acute myocardial infarction (AMI) in East Asian countries is limited and inconsistent. We aimed to investigate the association between air pollution and AMI hospitalizations in Chongqing, China. This time-stratified case-crossover study included 872 patients with AMI from three hospitals in Chongqing from January 2015 to December 2016. Exposures were compared between days with AMI (case days) and days without AMI (control days). Spearman’s correlation coefficient was applied to explore the correlation between air pollutants and meteorological conditions. Conditional logistic regression was used to assess the associations between air pollution exposure with different lag periods and AMI hospitalizations. Stratification analysis was further implemented by sex, age, and season. Hospitalizations for AMI were signifficantly associated with air pollution. All analyzed air pollutants showed lag-specific at lag 0 day and lag 01 day, whereas a 10 μg/m3 increase of average concentrations in PM2.5, PM10, SO2, NO2, and CO was associated with 1.034% (95% CI: 1.003–1.067%), 1.035% (95% CI:1.015–1.056%), 1.231% (95% CI: 1.053–1.438%), 1.062% (95% CI: 1.018–1.107%), and 1.406% (95% CI: 1.059–1.866%) increase in hospitalizations for AMI, respectively. No effect modifications were detected for sex, age, and season. Our findings suggest that short-term exposure to PM2.5, PM10, SO2, NO2, and CO contributes to increase AMI hospitalizations, which have public health implications for primary prevention and emergency health services.","htmlAbstract":"\u003cp\u003ePrevious studies have demonstrated that short-term exposure to ambient air pollution was associated with hospital admissions for cardiovascular diseases, but the evidence of its effects on acute myocardial infarction (AMI) in East Asian countries is limited and inconsistent. We aimed to investigate the association between air pollution and AMI hospitalizations in Chongqing, China. This time-stratified case-crossover study included 872 patients with AMI from three hospitals in Chongqing from January 2015 to December 2016. Exposures were compared between days with AMI (case days) and days without AMI (control days). Spearman’s correlation coefficient was applied to explore the correlation between air pollutants and meteorological conditions. Conditional logistic regression was used to assess the associations between air pollution exposure with different lag periods and AMI hospitalizations. Stratification analysis was further implemented by sex, age, and season. Hospitalizations for AMI were signifficantly associated with air pollution. All analyzed air pollutants showed lag-specific at lag 0\u0026nbsp;day and lag 01\u0026nbsp;day, whereas a 10\u0026nbsp;μg/m\u003csup\u003e3\u003c/sup\u003e increase of average concentrations in PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO was associated with 1.034% (95% CI: 1.003–1.067%), 1.035% (95% CI:1.015–1.056%), 1.231% (95% CI: 1.053–1.438%), 1.062% (95% CI: 1.018–1.107%), and 1.406% (95% CI: 1.059–1.866%) increase in hospitalizations for AMI, respectively. No effect modifications were detected for sex, age, and season. Our findings suggest that short-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO contributes to increase AMI hospitalizations, which have public health implications for primary prevention and emergency health services.\u003c/p\u003e","authors":[{"fullName":"Mingming Zhao","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1802889/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1802889/overview","affiliation":{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null},"affiliations":[{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null}],"nessieId":null},{"fullName":"Xing Liu","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null},"affiliations":[{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null}],"nessieId":null},{"fullName":"Ming Yuan","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Chongqing Medical and Pharmaceutical College","address":null},"affiliations":[{"name":"Chongqing Medical and Pharmaceutical College","address":null}],"nessieId":null},{"fullName":"Ying Yang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Bureau of Ecology and Environment of Jiulongpo District","address":null},"affiliations":[{"name":"Bureau of Ecology and Environment of Jiulongpo District","address":null}],"nessieId":null},{"fullName":"Hao Chen","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1451100/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1451100/overview","affiliation":{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null},"affiliations":[{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null}],"nessieId":"180389240479"},{"fullName":"Mengmeng Li","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null},"affiliations":[{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical 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Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null},"affiliations":[{"name":"Department of Epidemiology, School of Public Health and Management, Chongqing Medical University","address":null},{"name":"Nanan District Center for Disease Control and Prevention","address":null}],"nessieId":null},{"fullName":"Leqi Liu","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Jiangjin District Center for Disease Control and Prevention","address":null},"affiliations":[{"name":"Jiangjin District Center for Disease Control and Prevention","address":null}],"nessieId":null},{"fullName":"Li Zhou","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1808508/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1808508/overview","affiliation":{"name":"Department of Epidemiology, School of Public 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in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.941181","pubDate":"2022-07-22","score":4.426755249343731,"title":"Ambient Air Pollution and Hospitalization for Acute Myocardial Infarction in Chongqing, China: A Time-Stratified Case Crossover Analysis","topics":["Air Pollution","Hospitalization","environment","risk factor","acute myocardial infarction"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.941181/pdf"},{"__typename":"Feed_Article","_id":"6859a690d0a568fbb5007bcf","abstract":"With the rise and large-scale applications of social networking service, the prediction of information cascades has attracted extensive attention of researchers. User influence is an important factor affecting the dissemination of posts in online social networks. However, current studies usually take the number of users’ neighbors as their influence, and do not accurately describe the role of participating users in information dissemination. In this paper, a prediction model of information cascades in social networks is established based on the Hawkes process, and the model considers three factors, i.e., post influence, user influence and users’ response time, to describe the occurrence probability of forwarding events. In order to utilize abundant information of local network topology, we present a new method of calculating user influence, combining with semi-local centrality and local clustering coefficients. Then, a regression tree algorithm is used to determine time correction coefficients to reveal dynamic post influence, and the popularity prediction of posts in social networks is realized. Comparison experiments of different models are carried out on real-world datasets to evaluate the effectiveness and prediction performance of the proposed model, and results show that our method outperforms other counterparts.","htmlAbstract":"\u003cp\u003eWith the rise and large-scale applications of social networking service, the prediction of information cascades has attracted extensive attention of researchers. User influence is an important factor affecting the dissemination of posts in online social networks. However, current studies usually take the number of users\u0026#x2019; neighbors as their influence, and do not accurately describe the role of participating users in information dissemination. In this paper, a prediction model of information cascades in social networks is established based on the Hawkes process, and the model considers three factors, i.e., post influence, user influence and users\u0026#x2019; response time, to describe the occurrence probability of forwarding events. In order to utilize abundant information of local network topology, we present a new method of calculating user influence, combining with semi-local centrality and local clustering coefficients. Then, a regression tree algorithm is used to determine time correction coefficients to reveal dynamic post influence, and the popularity prediction of posts in social networks is realized. Comparison experiments of different models are carried out on real-world datasets to evaluate the effectiveness and prediction performance of the proposed model, and results show that our method outperforms other counterparts.\u003c/p\u003e","authors":[{"fullName":"Yingsi Zhao","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1820536/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1820536/overview","affiliation":{"name":"School of Economics and Management","address":null},"affiliations":[{"name":"School of Economics and Management","address":null}],"nessieId":"274878561442"},{"fullName":"Chu Zhong","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1831887/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1831887/overview","affiliation":{"name":"School of Electronics and Information Engineering","address":null},"affiliations":[{"name":"School of Electronics and Information Engineering","address":null}],"nessieId":"506806801552"}],"dates":{"acceptedDate":"2022-06-17","recentDate":"2022-07-08"},"doi":"10.3389/fphy.2022.951729","frontiersExtra":{"articleType":"Original Research","impact":{"citations":1,"crossrefCitations":0,"downloads":828,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":2709},"isPartOfResearchTopic":true,"isPublished":true,"section":"Social Physics"},"guid":951729,"images":[{"height":167,"url":"https://www.frontiersin.org/files/myhome article library/951729/951729_Thumb_400.jpg","width":400,"caption":null},{"height":275,"url":"https://www.frontiersin.org/files/Articles/951729/fphy-10-951729-HTML/image_m/fphy-10-951729-g001.jpg","width":809,"caption":"Overview of the proposed framework."},{"height":254,"url":"https://www.frontiersin.org/files/Articles/951729/fphy-10-951729-HTML/image_m/fphy-10-951729-g002.jpg","width":512,"caption":"Graphical representation of the model for the arrival process."},{"height":273,"url":"https://www.frontiersin.org/files/Articles/951729/fphy-10-951729-HTML/image_m/fphy-10-951729-g003.jpg","width":512,"caption":"Illustration of user influence analysis."},{"height":336,"url":"https://www.frontiersin.org/files/Articles/951729/fphy-10-951729-HTML/image_m/fphy-10-951729-g004.jpg","width":809,"caption":"Comparison of APE of two models on dataset 1. (A) Evolutionary trend of the median APE with prediction time t, (B) Evolutionary trend of mean APE with prediction time t."},{"height":373,"url":"https://www.frontiersin.org/files/Articles/951729/fphy-10-951729-HTML/image_m/fphy-10-951729-g005.jpg","width":505,"caption":"Comparison of Kendall rank correlation for two models on dataset 1."},{"height":357,"url":"https://www.frontiersin.org/files/Articles/951729/fphy-10-951729-HTML/image_m/fphy-10-951729-g006.jpg","width":803,"caption":"Comparison of APE of different models on dataset 2. (A) Evolutionary trend of the median APE with prediction time t, (B) Evolutionary trend of mean APE with prediction time t."},{"height":417,"url":"https://www.frontiersin.org/files/Articles/951729/fphy-10-951729-HTML/image_m/fphy-10-951729-g007.jpg","width":505,"caption":"Comparison of Kendall rank correlation for different models on dataset 2."}],"journal":{"guid":616,"name":"Frontiers in Physics","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fphy.2022.951729","pubDate":"2022-07-08","score":4.035542432195887,"title":"Cascade Prediction With Self-Exciting Point Process and Local User Influence Measurement","topics":["Social network","user influence","Self-exciting point process","Cascade prediction","Dynamic post correction"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fphy.2022.951729/pdf"}]],"pageParams":[null]},"dataUpdateCount":1,"dataUpdatedAt":1770588061280,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":["research-topic-articles",37251,1],"queryHash":"[\"research-topic-articles\",37251,1]"},{"state":{"data":{"researchTopicId":37251,"articleViews":57379,"articleDownloads":15772,"topicViews":2240,"summary":75391},"dataUpdateCount":1,"dataUpdatedAt":1770588061271,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":["research-topic-impact",37251],"queryHash":"[\"research-topic-impact\",37251]"}]}},"__N_SSG":true},"page":"/research-topics/[id]/[slug]/mag","query":{"id":"37251","slug":"network-mining-and-propagation-dynamics-analysis"},"buildId":"hka0MC3nELCCVBNOBVlqL","assetPrefix":"/_rtmag","isFallback":false,"gsp":true,"scriptLoader":[{"id":"google-analytics","strategy":"afterInteractive","children":"(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':\n new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],\n j=d.createElement(s),dl=l!='dataLayer'?'\u0026l='+l:'';j.async=true;j.src=\n 'https://tag-manager.frontiersin.org/gtm.js?id='+i+dl+ '\u0026gtm_auth=PYjuAXuPWCihEq8Nf7ErrA\u0026gtm_preview=env-1\u0026gtm_cookies_win=x';f.parentNode.insertBefore(j,f);\n })(window,document,'script','dataLayer','GTM-PT9D93K');"}]}