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Related Concept Videos

Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Related Experiment Video

Updated: Jun 13, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts.

Nora Al-Naami, Nicolas Medoc, Matteo Magnani

    IEEE Transactions on Visualization and Computer Graphics
    |September 11, 2024
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    Summary
    This summary is machine-generated.

    Orderable node-link diagrams significantly improve cluster identification in graphs. Users accurately and quickly identified clusters using these diagrams compared to force-directed layouts, especially for less distinct clusters.

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    Area of Science:

    • Graph visualization
    • Data analysis
    • Information visualization

    Background:

    • Graphs model relationships, and cluster visualization aids insight discovery in various fields.
    • Force-directed layouts enhance cluster visibility but lack intuitive node ordering.
    • Matrix layouts offer ordering but lack a node-link metaphor.

    Purpose of the Study:

    • To investigate the impact of node ordering on cluster visual saliency in orderable node-link diagrams.
    • To compare the effectiveness of orderable node-link diagrams against state-of-the-art force-directed graph layout algorithms.

    Main Methods:

    • Crowdsourced controlled experiment.
    • Evaluation of radial diagrams, arc diagrams, and symmetric arc diagrams.
    • Comparison with 'Linlog', 'Backbone', and 'sfdp' force-directed layouts.

    Main Results:

    • Users counted clusters more accurately and faster with orderable node-link diagrams.
    • The advantage was more pronounced with low cluster separability and/or compactness.
    • Orderable node-link diagrams outperformed tested force-directed algorithms.

    Conclusions:

    • Node ordering in node-link diagrams enhances cluster visualization.
    • Orderable node-link diagrams offer a more effective alternative to force-directed layouts for cluster identification.
    • These findings are crucial for applications requiring clear graph cluster representation.