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Related Concept Videos

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Multiple Bar Graph

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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.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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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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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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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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The R Chart01:02

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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
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Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
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Graph visualization efficiency of popular web-based libraries.

Xin Zhao1, Xuan Wang1, Xianzhe Zou1

  • 1School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.

Visual Computing for Industry, Biomedicine, and Art
|May 8, 2025
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Summary
This summary is machine-generated.

This study empirically evaluates web-based graph visualization libraries like D3.js, ECharts.js, and G6.js. It provides guidelines to help users select the best library based on efficiency needs for node-link graph visualizations.

Keywords:
Graph visualizationNode-link diagramVisualization libraryWeb-based visualization

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Area of Science:

  • Computer Science
  • Data Visualization
  • Human-Computer Interaction

Background:

  • Web-based libraries (D3.js, ECharts.js, G6.js) are crucial for node-link graph visualizations.
  • Current research lacks practical, library-specific performance evaluations, hindering user selection.
  • Efficiency is key, with demands like visualizing 3k nodes/4k edges within 1 min at 30 fps.

Purpose of the Study:

  • To empirically evaluate the performance of popular web-based graph visualization libraries.
  • To bridge the gap between theoretical algorithm research and practical library selection.
  • To provide application-oriented guidelines for choosing libraries based on efficiency requirements.

Main Methods:

  • Conducted experiments using popular libraries (D3.js, ECharts.js, G6.js) and diverse graph datasets (100-200k nodes, 1-10 edge-to-node ratios).
  • Recorded time costs and frame rates for visualizing datasets with each library.
  • Analyzed performance characteristics and developed user-friendly guidelines.

Main Results:

  • Quantified performance differences between libraries across various graph scales and densities.
  • Identified specific strengths and weaknesses of each library concerning visualization efficiency.
  • Generated empirical data on time costs and frame rates for practical decision-making.

Conclusions:

  • The study offers practical, data-driven guidelines for selecting web-based graph visualization libraries.
  • Users can now make informed choices based on specific efficiency needs and dataset characteristics.
  • Recommendations aid in quickly identifying suitable libraries for node-link graph visualization tasks.