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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

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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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Multiple Bar Graph01:07

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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Ogive Graph01:07

Ogive Graph

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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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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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Graphs of Functions01:30

Graphs of Functions

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

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Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
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Related Experiment Video

Updated: Feb 23, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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VIGOR: Interactive Visual Exploration of Graph Query Results.

Robert Pienta, Fred Hohman, Alex Endert

    IEEE Transactions on Visualization and Computer Graphics
    |September 4, 2017
    PubMed
    Summary
    This summary is machine-generated.

    VIGOR, a visual analytics system, helps analysts understand complex graph query results. It enables faster, more accurate pattern discovery in cybersecurity and other domains.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Network Analysis

    Background:

    • Graph pattern discovery is crucial in diverse fields like cybersecurity and finance.
    • Analyzing large sets of subgraph query results presents significant visualization and sensemaking challenges.
    • Existing research often overlooks effective summarization and exploration techniques for complex graph query outputs.

    Purpose of the Study:

    • To introduce VIGOR, an interactive visual analytics system designed for exploring and understanding graph query results.
    • To address the limitations in summarizing and visualizing numerous subgraphs with shared node values, rich features, and flexible structures.
    • To enhance the sensemaking process for analysts dealing with large-scale graph data.

    Main Methods:

    • VIGOR employs multiple coordinated views for diverse data representation and organization.
    • It features an exemplar-based interaction technique allowing analysts to refine or broaden search criteria.
    • A novel feature-aware subgraph result summarization method is utilized to condense complex results.

    Main Results:

    • VIGOR facilitates the discovery of security blindspots in a large cybersecurity dataset.
    • A within-subjects study demonstrated VIGOR's ease of use compared to a leading graph database system.
    • Analysts using VIGOR achieved higher speed and made fewer errors in understanding query results.

    Conclusions:

    • VIGOR offers an effective solution for visualizing and analyzing complex graph query results.
    • The system enhances analysts' ability to derive insights from large graph datasets.
    • VIGOR shows significant potential for real-world applications, particularly in cybersecurity analysis.