The advent of the time of big data along with social networks makes the visualization and analysis of networks information become increasingly important in many fields. Based on the information from social networks, t...The advent of the time of big data along with social networks makes the visualization and analysis of networks information become increasingly important in many fields. Based on the information from social networks, the idea of information visualization and development of tools are presented. Popular social network micro-blog （＇Weibo＇） is chosen to realize the process of users＇ interest and communications data analysis. User interest visualization methods are discussed and chosen and programs are developed to collect users＇ interest and describe it by graph. The visualization results may be used to provide the commercial recommendation or social investigation application for decision makers.展开更多
Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this conte...Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this context, it is important to know how allies and opponents are positioned, in order to understand the discussion dynamics and plan adequate actions. This paper suggests the use of social network visualizations to explicit oppositions and alliances in order to support the understanding and following of political discussions. A system which supports these visualizations was built. An experiment performed to test the proposed visualizations showed to which extent they can be more efficient in identifying information about clashes and alliances than an online discussion system can.展开更多
Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility ...Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.展开更多
Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhib...Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.展开更多
Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily de...Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in atgribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.展开更多
Visualizing social networks,especially an overview emphasizing their structure,i.e.,communities and their interconnections,is known to be a challenging problem.In this paper,we present a set of design rationales to bu...Visualizing social networks,especially an overview emphasizing their structure,i.e.,communities and their interconnections,is known to be a challenging problem.In this paper,we present a set of design rationales to build such overview visualizations of social networks and our solution called Jasper.We evaluate its performances against two of the most wide-spread visualization techniques(matrix and node-link diagram)in a human–computer controlled experiment based on community-related tasks.While none of the techniques emerge as the overwhelming winner,Jasper appears to be one of the best methods for each task;a fact sustained by the marks given by the users.Overall,Jasper can be seen as an all-encompassing solution for quickly producing legible and compact overviews of large social networks on a single modern computer.展开更多
To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spr...To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spread and stopped in a short span of time.Both targets can be achieved,since network of information can be extended and as well destroyed.So,information spread and community formation have become one of the most crucial issues in the world of SNA(Social Network Analysis).In this work,the complex network of twitter social network has been formalized and results are analyzed.For this purpose,different network metrics have been utilized.Visualization of the network is provided in its original form and then filter out(different percentages)from the network to eliminate the less impacting nodes and edges for better analysis.This network is analyzed according to different centrality measures,like edge-betweenness,betweenness centrality,closeness centrality and eigenvector centrality.Influential nodes are detected and their impact is observed on the network.The communities are analyzed in terms of network coverage considering theMinimum Spanning Tree,shortest path distribution and network diameter.It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook,Instagram,Twitter,LinkedIn,etc.展开更多
Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings...Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing.However,because of the present lockdown measures in several countries,the validation of computer vision systems using large-scale datasets is a challenge.Methods In this paper,a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality(VR)technology.Using VR,we modeled a digital twin(DT)of an existing office space and used it to create a dataset of individuals in different postures,dresses,and locations.To test the proposed solution,we implemented a convolutional neural network(CNN)model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures.Results We detected the number of persons in both the real and synthetic datasets with more than 90%accuracy,and the actual and measured distances were significantly correlated(r=0.99).Finally,we used intermittent-layer-and heatmap-based data visualization techniques to explain the failure modes of a CNN.Conclusions A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals.The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.展开更多
目的 应用CiteSpace可视化软件系统梳理近十年甲状腺超声人工智能领域的研究进展、知识结构。方法 以中国知网及Web of Science为来源数据库,检索2013~2023年关于甲状腺超声人工智能的相关文献,应用CiteSpace可视化软件绘制中、英文文...目的 应用CiteSpace可视化软件系统梳理近十年甲状腺超声人工智能领域的研究进展、知识结构。方法 以中国知网及Web of Science为来源数据库,检索2013~2023年关于甲状腺超声人工智能的相关文献,应用CiteSpace可视化软件绘制中、英文文献作者、机构、关键词图谱,并进行文献计量分析。结果 共纳入9515篇文献,涉及34个机构,119个关键词。知识图谱显示,中文文献发文量整体较英文文献发文量高,中、英文文献发文量分别自2013、2018年起逐年上升,近两年增长速度减缓。发表中文文献较多的作者包括姜珏、詹维伟、罗渝昆、周琦、雷小莹、张波等,发表英文文献较多的作者包括Paul、Saba、Suri等。国内231个机构、国际226个机构发表了相关文献,其中中文文献发文量前三的机构包括上海交通大学医学院附属瑞金医院超声科(21篇)、中国医学科学院北京协和医学院北京协和医院超声医学科(10篇)、西安交通大学第二附属医院超声研究室(5篇)和中国医科大学附属盛京医院超声科(5篇);英文文献发文量前三的机构包括浙江大学(9篇)、上海交通大学(7篇)、中山大学(7篇)和华中科技大学(7篇)。机构间合作关系主要以浙江大学、上海交通大学为核心的国内机构,以及以North Eastern Hill Univ为核心的国际机构。关键词分析结果显示,中文文献主要集中在多模态超声用于甲状腺良恶性结节的鉴别方面;英文文献更偏向于机器深度学习、人工智能方向。结论 国内及国际研究人员对于甲状腺超声人工智能的关注度不断提高,但仍需加强跨机构、跨团队、跨区域的多中心协作,进一步深入研究。展开更多
文摘The advent of the time of big data along with social networks makes the visualization and analysis of networks information become increasingly important in many fields. Based on the information from social networks, the idea of information visualization and development of tools are presented. Popular social network micro-blog （＇Weibo＇） is chosen to realize the process of users＇ interest and communications data analysis. User interest visualization methods are discussed and chosen and programs are developed to collect users＇ interest and describe it by graph. The visualization results may be used to provide the commercial recommendation or social investigation application for decision makers.
文摘Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this context, it is important to know how allies and opponents are positioned, in order to understand the discussion dynamics and plan adequate actions. This paper suggests the use of social network visualizations to explicit oppositions and alliances in order to support the understanding and following of political discussions. A system which supports these visualizations was built. An experiment performed to test the proposed visualizations showed to which extent they can be more efficient in identifying information about clashes and alliances than an online discussion system can.
文摘Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62476045)the LiaoNing Revitalization Talents Program(Grant No.XLYC1807106)the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning(Grant No.LR2016070).
文摘Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
基金FAPESP, CNPq and CAPES for their financial support
文摘Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in atgribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.
文摘Visualizing social networks,especially an overview emphasizing their structure,i.e.,communities and their interconnections,is known to be a challenging problem.In this paper,we present a set of design rationales to build such overview visualizations of social networks and our solution called Jasper.We evaluate its performances against two of the most wide-spread visualization techniques(matrix and node-link diagram)in a human–computer controlled experiment based on community-related tasks.While none of the techniques emerge as the overwhelming winner,Jasper appears to be one of the best methods for each task;a fact sustained by the marks given by the users.Overall,Jasper can be seen as an all-encompassing solution for quickly producing legible and compact overviews of large social networks on a single modern computer.
文摘To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spread and stopped in a short span of time.Both targets can be achieved,since network of information can be extended and as well destroyed.So,information spread and community formation have become one of the most crucial issues in the world of SNA(Social Network Analysis).In this work,the complex network of twitter social network has been formalized and results are analyzed.For this purpose,different network metrics have been utilized.Visualization of the network is provided in its original form and then filter out(different percentages)from the network to eliminate the less impacting nodes and edges for better analysis.This network is analyzed according to different centrality measures,like edge-betweenness,betweenness centrality,closeness centrality and eigenvector centrality.Influential nodes are detected and their impact is observed on the network.The communities are analyzed in terms of network coverage considering theMinimum Spanning Tree,shortest path distribution and network diameter.It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook,Instagram,Twitter,LinkedIn,etc.
文摘Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing.However,because of the present lockdown measures in several countries,the validation of computer vision systems using large-scale datasets is a challenge.Methods In this paper,a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality(VR)technology.Using VR,we modeled a digital twin(DT)of an existing office space and used it to create a dataset of individuals in different postures,dresses,and locations.To test the proposed solution,we implemented a convolutional neural network(CNN)model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures.Results We detected the number of persons in both the real and synthetic datasets with more than 90%accuracy,and the actual and measured distances were significantly correlated(r=0.99).Finally,we used intermittent-layer-and heatmap-based data visualization techniques to explain the failure modes of a CNN.Conclusions A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals.The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.
文摘目的 应用CiteSpace可视化软件系统梳理近十年甲状腺超声人工智能领域的研究进展、知识结构。方法 以中国知网及Web of Science为来源数据库,检索2013~2023年关于甲状腺超声人工智能的相关文献,应用CiteSpace可视化软件绘制中、英文文献作者、机构、关键词图谱,并进行文献计量分析。结果 共纳入9515篇文献,涉及34个机构,119个关键词。知识图谱显示,中文文献发文量整体较英文文献发文量高,中、英文文献发文量分别自2013、2018年起逐年上升,近两年增长速度减缓。发表中文文献较多的作者包括姜珏、詹维伟、罗渝昆、周琦、雷小莹、张波等,发表英文文献较多的作者包括Paul、Saba、Suri等。国内231个机构、国际226个机构发表了相关文献,其中中文文献发文量前三的机构包括上海交通大学医学院附属瑞金医院超声科(21篇)、中国医学科学院北京协和医学院北京协和医院超声医学科(10篇)、西安交通大学第二附属医院超声研究室(5篇)和中国医科大学附属盛京医院超声科(5篇);英文文献发文量前三的机构包括浙江大学(9篇)、上海交通大学(7篇)、中山大学(7篇)和华中科技大学(7篇)。机构间合作关系主要以浙江大学、上海交通大学为核心的国内机构,以及以North Eastern Hill Univ为核心的国际机构。关键词分析结果显示,中文文献主要集中在多模态超声用于甲状腺良恶性结节的鉴别方面;英文文献更偏向于机器深度学习、人工智能方向。结论 国内及国际研究人员对于甲状腺超声人工智能的关注度不断提高,但仍需加强跨机构、跨团队、跨区域的多中心协作,进一步深入研究。
