Interaction is increasingly integrating into data stories to support data exploration and explanation. Interaction can also be combined with the narrative device, breaking the fourth wall (BTFW), to build a deeper con...
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Pictorial visualizations portray data with figurative messages and approximate the audience to the visualization. Previous research on pictorial visualizations has developed authoring tools or generation systems, but ...
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Recent interest in design through the artificial intelligence (AI) lens is rapidly increasing. Designers, as a special user group interacting with AI, have received more attention in the Human-Computer Interaction com...
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As an effective form of narrative visualization, visual data stories are widely used in data-driven storytelling to communicate complex insights and support data understanding. Although important, they are difficult t...
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With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large *** several visualization recommendation systems have been propose...
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With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large *** several visualization recommendation systems have been proposed,so far,the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the *** this paper,we proposed AVA,an open-sourced web-based framework for Automated Visual *** contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights *** code is available at https://***/antvis/AVA.
To solve the 2024 VAST Challenge MC3, we use PageRank and different filtering techniques to select nodes or components of interest. We then use TimeArc, a datavisualization technique to visualize the evolution of the...
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Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep ***,choosing the right method for different tasks can be *** within network...
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Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep ***,choosing the right method for different tasks can be *** within networks help reveal underlying structures and *** how different models preserve community properties is crucial for identifying the best graph representation for data *** paper defines indicators to explore the perceptual quality of community properties in representation learning spaces,including the consistency of community structure,node distribution within and between communities,and central node distribution.A visualization system presents these indicators,allowing users to evaluate models based on community *** studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models.
visualization dashboards serve as an information presentation that uses a tiled layout of key metrics visualized in charts for collaborative decision-making. Existing work has developed tools and techniques for comput...
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In recent years, the pivotal role of emotions in information dissemination has attracted extensive attention. Research indicates that emotionally charged content significantly outperforms neutral content in disseminat...
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Freeform thin-shell surfaces are critical in various fields, but their fabrication is complex and costly. Traditional methods are wasteful and require custom molds, while 3D printing needs extensive support structures...
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