Pub Date : 2022-05-21DOI: 10.1177/14738716221096383
J. Borges
Genealogy studies are growing in popularity, and researchers are increasingly using visualization methods to summarize and communicate their findings. A family tree is a visual representation of family members and their relationships that is commonly used to support the research of a family’s history and publish the results. In some cases, an ancestor may occur in more than one place in the lineage of an individual, which is one of the reasons for the occurrence of consanguineous marriages, that is, marriages between blood relative spouses. Current methods for family tree visualization were not designed to analyze and assess the level of consanguinity in the ancestry of individuals. This paper proposes VisAC, an interactive tool to support the visual analysis of consanguinity in individuals’ ancestry. The inbreeding coefficient is used as a measure of consanguinity. The coefficient corresponds to an estimate of the probability that two alleles (a variant of a given gene) in the DNA were inherited from the same individual. A visualization design and an interactive tool were developed with genealogists’ support. In addition, the feedback collected through a questionnaire about two demo videos and tests with three target users strongly supports the effectiveness of the family tree visual representation and the adequacy of the interactive tool for the exploratory analysis task. Real-world examples are given to illustrate the usefulness of the visualization design, and an example of exploratory analysis is presented to illustrate the use of the interactive tool. In summary, this work formulates the task of visual analysis of consanguinity in ancestors’ trees and proposes VisAC, a new visualization tool to support the task.
{"title":"VisAC: An interactive tool for visual analysis of consanguinity in the ancestry of individuals","authors":"J. Borges","doi":"10.1177/14738716221096383","DOIUrl":"https://doi.org/10.1177/14738716221096383","url":null,"abstract":"Genealogy studies are growing in popularity, and researchers are increasingly using visualization methods to summarize and communicate their findings. A family tree is a visual representation of family members and their relationships that is commonly used to support the research of a family’s history and publish the results. In some cases, an ancestor may occur in more than one place in the lineage of an individual, which is one of the reasons for the occurrence of consanguineous marriages, that is, marriages between blood relative spouses. Current methods for family tree visualization were not designed to analyze and assess the level of consanguinity in the ancestry of individuals. This paper proposes VisAC, an interactive tool to support the visual analysis of consanguinity in individuals’ ancestry. The inbreeding coefficient is used as a measure of consanguinity. The coefficient corresponds to an estimate of the probability that two alleles (a variant of a given gene) in the DNA were inherited from the same individual. A visualization design and an interactive tool were developed with genealogists’ support. In addition, the feedback collected through a questionnaire about two demo videos and tests with three target users strongly supports the effectiveness of the family tree visual representation and the adequacy of the interactive tool for the exploratory analysis task. Real-world examples are given to illustrate the usefulness of the visualization design, and an example of exploratory analysis is presented to illustrate the use of the interactive tool. In summary, this work formulates the task of visual analysis of consanguinity in ancestors’ trees and proposes VisAC, a new visualization tool to support the task.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47730741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-21DOI: 10.1177/14738716221086589
Youngjoo Kim, Alexandru C Telea, Scott C Trager, Jos BTM Roerdink
Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory a...
将降维(DR)应用于大型高维数据集，在2D投影中区分潜在的高维数据簇是一项挑战。
{"title":"Visual cluster separation using high-dimensional sharpened dimensionality reduction","authors":"Youngjoo Kim, Alexandru C Telea, Scott C Trager, Jos BTM Roerdink","doi":"10.1177/14738716221086589","DOIUrl":"https://doi.org/10.1177/14738716221086589","url":null,"abstract":"Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory a...","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-21DOI: 10.1177/14738716221085965
O. Christmann, Sylvain Fleury, J. Migaud, Vincent Raimbault, Benjamin Poussard, Thibaut Guitter, G. Gorisse, S. Richir
This study presents two experiments addressing the representation of scientific data, in particular airflows, with a user-centered design approach. Our objective is to provide users feedback to data visualization designers to help them choose an air flow representation that is understandable and attractive for non-experts. The first study focuses on static markers allowing to visualize an airflow, with information characterizing the direction and the intensity. In a second study, carried out in an immersive virtual environment, two information were added, the temporal evolution and the concentration of pollutants in the air. To measure comprehension and attractiveness, participants were asked to answer items on Likert scales (experiment 1) and to answer User Experience Questionnaire (experiment 2). The results revealed that arrows seem to be a very common and understandable form to represent orientation and direction of flow, but that they should be improved to be more attractive by making them brighter and more transparent, as the representation could occlude the scene, especially in virtual reality. To solve this problem, we suggest giving the users the ability to define the specific area where they want to see the air flow, using a cross-sectional view. Vector fields and streamlines could therefore be applied in a virtual reality context.
{"title":"Visualizing the invisible: User-centered design of a system for the visualization of flows and concentrations of particles in the air","authors":"O. Christmann, Sylvain Fleury, J. Migaud, Vincent Raimbault, Benjamin Poussard, Thibaut Guitter, G. Gorisse, S. Richir","doi":"10.1177/14738716221085965","DOIUrl":"https://doi.org/10.1177/14738716221085965","url":null,"abstract":"This study presents two experiments addressing the representation of scientific data, in particular airflows, with a user-centered design approach. Our objective is to provide users feedback to data visualization designers to help them choose an air flow representation that is understandable and attractive for non-experts. The first study focuses on static markers allowing to visualize an airflow, with information characterizing the direction and the intensity. In a second study, carried out in an immersive virtual environment, two information were added, the temporal evolution and the concentration of pollutants in the air. To measure comprehension and attractiveness, participants were asked to answer items on Likert scales (experiment 1) and to answer User Experience Questionnaire (experiment 2). The results revealed that arrows seem to be a very common and understandable form to represent orientation and direction of flow, but that they should be improved to be more attractive by making them brighter and more transparent, as the representation could occlude the scene, especially in virtual reality. To solve this problem, we suggest giving the users the ability to define the specific area where they want to see the air flow, using a cross-sectional view. Vector fields and streamlines could therefore be applied in a virtual reality context.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48560755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01Epub Date: 2021-09-22DOI: 10.1177/14738716211045354
Aristides Mairena, Carl Gutwin, Andy Cockburn
Emphasis effects are visual changes that make data elements distinct from their surroundings. Designers may use computational saliency models to predict how a viewer's attention will be guided by a specific effect; however, although saliency models provide a foundational understanding of emphasis perception, they only cover specific visual effects in abstract conditions. To address these limitations, we carried out crowdsourced studies that evaluate emphasis perception in a wider range of conditions than previously studied. We varied effect magnitude, distractor number and type, background, and visualization type, and measured the perceived emphasis of 12 visual effects. Our results show that there are perceptual commonalities of emphasis across a wide range of environments, but also that there are limitations on perceptibility for some effects, dependent on a visualization's background or type. We developed a model of emphasis predictability based on simple scatterplots that can be extended to other viewing conditions. Our studies provide designers with new understanding of how viewers experience emphasis in realistic visualization settings.
{"title":"Which emphasis technique to use? Perception of emphasis techniques with varying distractors, backgrounds, and visualization types.","authors":"Aristides Mairena, Carl Gutwin, Andy Cockburn","doi":"10.1177/14738716211045354","DOIUrl":"https://doi.org/10.1177/14738716211045354","url":null,"abstract":"<p><p>Emphasis effects are visual changes that make data elements distinct from their surroundings. Designers may use computational saliency models to predict how a viewer's attention will be guided by a specific effect; however, although saliency models provide a foundational understanding of emphasis perception, they only cover specific visual effects in abstract conditions. To address these limitations, we carried out crowdsourced studies that evaluate emphasis perception in a wider range of conditions than previously studied. We varied effect magnitude, distractor number and type, background, and visualization type, and measured the perceived emphasis of 12 visual effects. Our results show that there are perceptual commonalities of emphasis across a wide range of environments, but also that there are limitations on perceptibility for some effects, dependent on a visualization's background or type. We developed a model of emphasis predictability based on simple scatterplots that can be extended to other viewing conditions. Our studies provide designers with new understanding of how viewers experience emphasis in realistic visualization settings.</p>","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39630510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-10DOI: 10.1177/14738716221081831
Elif E. Firat, Alark Joshi, R. Laramee
With the advent of novel visualization techniques to convey complex information, data visualization literacy is growing in importance. Two facets of literacy are user understanding and the discovery of visual patterns with the help of graphical representations. The research literature on visualization literacy provides useful guidance and important opportunities for further studies in this field. This survey examines and classifies prior research on visualization literacy that analyzes how well users understand novel data representations. To our knowledge, this is the first comprehensive survey paper with a focus on interactive visualization literacy. We categorize existing relevant research into unique subject groups that facilitate and inform comparisons of related literature and provide an overview of the same. Additionally, the survey/classification also provides an overview of the various evaluation techniques used in this field of research due to their challenging nature. Our novel classification enables researchers to find both mature and unexplored directions that may lead to future work. This survey serves as a valuable resource for both beginners and experienced researchers interested in the topic of visualization literacy.
{"title":"Interactive visualization literacy: The state-of-the-art","authors":"Elif E. Firat, Alark Joshi, R. Laramee","doi":"10.1177/14738716221081831","DOIUrl":"https://doi.org/10.1177/14738716221081831","url":null,"abstract":"With the advent of novel visualization techniques to convey complex information, data visualization literacy is growing in importance. Two facets of literacy are user understanding and the discovery of visual patterns with the help of graphical representations. The research literature on visualization literacy provides useful guidance and important opportunities for further studies in this field. This survey examines and classifies prior research on visualization literacy that analyzes how well users understand novel data representations. To our knowledge, this is the first comprehensive survey paper with a focus on interactive visualization literacy. We categorize existing relevant research into unique subject groups that facilitate and inform comparisons of related literature and provide an overview of the same. Additionally, the survey/classification also provides an overview of the various evaluation techniques used in this field of research due to their challenging nature. Our novel classification enables researchers to find both mature and unexplored directions that may lead to future work. This survey serves as a valuable resource for both beginners and experienced researchers interested in the topic of visualization literacy.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42974031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-07DOI: 10.1177/14738716221079591
Sriram Karthik Badam, Senthil K. Chandrasegaran, N. Elmqvist
Multidimensional data is often visualized using coordinated multiple views in an interactive dashboard. However, unlike in infographics where text is often a central part of the presentation, there is currently little knowledge of how to best integrate text and annotations in a visualization dashboard. In this paper, we explore a technique called FacetNotes for presenting these textual annotations on top of any visualization within a dashboard irrespective of the scale of data shown or the design of visual representation itself. FacetNotes does so by grouping and ordering the textual annotations based on properties of (1) the individual data points associated with the annotations, and (2) the target visual representation on which they should be shown. We present this technique along with a set of user interface features and guidelines to apply it to visualization interfaces. We also demonstrate FacetNotes in a custom visual dashboard interface. Finally, results from a user study of FacetNotes show that the technique improves the scope and complexity of insights developed during visual exploration.
{"title":"Integrating annotations into multidimensional visual dashboards","authors":"Sriram Karthik Badam, Senthil K. Chandrasegaran, N. Elmqvist","doi":"10.1177/14738716221079591","DOIUrl":"https://doi.org/10.1177/14738716221079591","url":null,"abstract":"Multidimensional data is often visualized using coordinated multiple views in an interactive dashboard. However, unlike in infographics where text is often a central part of the presentation, there is currently little knowledge of how to best integrate text and annotations in a visualization dashboard. In this paper, we explore a technique called FacetNotes for presenting these textual annotations on top of any visualization within a dashboard irrespective of the scale of data shown or the design of visual representation itself. FacetNotes does so by grouping and ordering the textual annotations based on properties of (1) the individual data points associated with the annotations, and (2) the target visual representation on which they should be shown. We present this technique along with a set of user interface features and guidelines to apply it to visualization interfaces. We also demonstrate FacetNotes in a custom visual dashboard interface. Finally, results from a user study of FacetNotes show that the technique improves the scope and complexity of insights developed during visual exploration.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44680680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1177/14738716221091487
M. Baroni, C. G. Silva
{"title":"A comparative analysis of matrix reordering algorithms regarding canonical data patterns","authors":"M. Baroni, C. G. Silva","doi":"10.1177/14738716221091487","DOIUrl":"https://doi.org/10.1177/14738716221091487","url":null,"abstract":"","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65676029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-29DOI: 10.1177/14738716211064921
Javad Yaali, Vincent Grégoire, Thomas Hurtut
High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.
{"title":"HFTViz: Visualization for the exploration of high frequency trading data","authors":"Javad Yaali, Vincent Grégoire, Thomas Hurtut","doi":"10.1177/14738716211064921","DOIUrl":"https://doi.org/10.1177/14738716211064921","url":null,"abstract":"High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42067256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-22DOI: 10.1177/14738716221079593
Brian Felipe Keith Norambuena, Tanushree Mitra, Chris North
Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in their narrative sensemaking process. Narrative maps allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model the connection between storylines. We seek to understand how analysts create and use narrative maps in order to obtain design guidelines for an interactive visualization tool for narrative maps that can aid analysts in narrative sensemaking. We perform two experiments with a data set of news articles. The insights extracted from our studies can be used to design narrative maps, extraction algorithms, and visual analytics tools to support the narrative sensemaking process. The contributions of this paper are three-fold: (1) an analysis of how analysts construct narrative maps; (2) a user evaluation of specific narrative map features; and (3) design guidelines for narrative maps. Our findings suggest ways for designing narrative maps and extraction algorithms, as well as providing insights toward useful interactions. We discuss these insights and design guidelines and reflect on the potential challenges involved. As key highlights, we find that narrative maps should avoid redundant connections that can be inferred by using the transitive property of event connections, reducing the overall complexity of the map. Moreover, narrative maps should use multiple types of cognitive connections between events such as topical and causal connections, as this emulates the strategies that analysts use in the narrative sensemaking process.
{"title":"Design guidelines for narrative maps in sensemaking tasks","authors":"Brian Felipe Keith Norambuena, Tanushree Mitra, Chris North","doi":"10.1177/14738716221079593","DOIUrl":"https://doi.org/10.1177/14738716221079593","url":null,"abstract":"Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in their narrative sensemaking process. Narrative maps allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model the connection between storylines. We seek to understand how analysts create and use narrative maps in order to obtain design guidelines for an interactive visualization tool for narrative maps that can aid analysts in narrative sensemaking. We perform two experiments with a data set of news articles. The insights extracted from our studies can be used to design narrative maps, extraction algorithms, and visual analytics tools to support the narrative sensemaking process. The contributions of this paper are three-fold: (1) an analysis of how analysts construct narrative maps; (2) a user evaluation of specific narrative map features; and (3) design guidelines for narrative maps. Our findings suggest ways for designing narrative maps and extraction algorithms, as well as providing insights toward useful interactions. We discuss these insights and design guidelines and reflect on the potential challenges involved. As key highlights, we find that narrative maps should avoid redundant connections that can be inferred by using the transitive property of event connections, reducing the overall complexity of the map. Moreover, narrative maps should use multiple types of cognitive connections between events such as topical and causal connections, as this emulates the strategies that analysts use in the narrative sensemaking process.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42839522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-15DOI: 10.1177/14738716211060306
G. Kortemeyer
The paper describes a method for the immersive, dynamic visualization of undirected, weighted graphs. Using the Fruchterman-Reingold method, force-directed graphs are drawn in a Virtual-Reality system. The user can walk through the data, as well as move vertices using controllers, while the network display rearranges in realtime according to Newtonian physics. In addition to the physics behind the employed method, the paper explains the most pertinent computational mechanisms for its implementation, using Unity, SteamVR, and a Virtual-Reality system such as HTC Vive (the source package is made available for download). It was found that the method allows for intuitive exploration of graphs with on the order of 10 2 vertices, and that dynamic extrusion of vertices and realtime readjustment of the network structure allows for developing an intuitive understanding of the relationship of a vertex to the remainder of the network. Based on this observation, possible future developments are suggested.
{"title":"Virtual-Reality graph visualization based on Fruchterman-Reingold using Unity and SteamVR","authors":"G. Kortemeyer","doi":"10.1177/14738716211060306","DOIUrl":"https://doi.org/10.1177/14738716211060306","url":null,"abstract":"The paper describes a method for the immersive, dynamic visualization of undirected, weighted graphs. Using the Fruchterman-Reingold method, force-directed graphs are drawn in a Virtual-Reality system. The user can walk through the data, as well as move vertices using controllers, while the network display rearranges in realtime according to Newtonian physics. In addition to the physics behind the employed method, the paper explains the most pertinent computational mechanisms for its implementation, using Unity, SteamVR, and a Virtual-Reality system such as HTC Vive (the source package is made available for download). It was found that the method allows for intuitive exploration of graphs with on the order of 10 2 vertices, and that dynamic extrusion of vertices and realtime readjustment of the network structure allows for developing an intuitive understanding of the relationship of a vertex to the remainder of the network. Based on this observation, possible future developments are suggested.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43541031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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