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Ogive Graph

Ogive Graph

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 type...

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A weather map provides a practical example of a function of two variables. Across a wide region such as the United States, temperatures vary from one location to another. Each location can be identified by two geographic coordinates: longitude and latitude. Since a single temperature value is assigned to each coordinate pair, the situation can be represented mathematically as a function with two inputs and one output.In mathematical notation, longitude and latitude can be labeled as x and y,...

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Degrees of Freedom

Degrees of Freedom

The degree of freedom for a particular statistical calculation is the number of values that are free to vary. As a result, the minimum number of independent numbers can specify a particular statistic. The degrees of freedom differ greatly depending on known and uncalculated statistical components.
For example, suppose there are three unknown numbers whose mean is 10; although we can freely assign values to the first and second numbers, the value of the last number can not be arbitrarily...

Degrees of Freedom

Degrees of Freedom

The degree of freedom for a particular statistical calculation is the number of values that are free to vary. Thus, the minimum number of independent numbers can specify a particular statistic. The degrees of freedom differ greatly depending on known and uncalculated statistical components.
For example, suppose there are three unknown numbers whose mean is 10; although we can freely assign values to the first and second numbers, the value of the last number can not be arbitrarily assigned.

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Related Experiment Video

Updated: Jun 19, 2026

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Published on: May 4, 2018

"Search, show context, expand on demand": supporting large graph exploration with degree-of-interest.

Frank van Ham¹, Adam Perer

¹IBM-ILOG Research in Gentilly, France. frankvanham@fr.ibm.com

IEEE Transactions on Visualization and Computer Graphics

|October 17, 2009

Summary

This study introduces a new method for exploring large graphs by focusing on the immediate context around a node. This approach is efficient for users with specific information needs and limited resources.

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Published on: June 13, 2025

Area of Science:

Computer Science
Information Visualization
Human-Computer Interaction

Background:

Traditional graph visualization often focuses on global overviews, which can be impractical for users with specific information needs or limited computational resources.
Analyzing entire large graphs is often unnecessary and resource-intensive, especially for online or mobile users.
Existing methods may not adequately address the need for focused exploration of relevant graph segments.

Purpose of the Study:

To propose and evaluate an interaction model for remotely browsing the immediate context graph around a specific node of interest.
To adapt Furnas' degree of interest function for efficient subgraph extraction in graph visualization.
To demonstrate a method for controlling visualization complexity and guiding users to relevant data points within a graph context.

Main Methods:

Adapted Furnas' degree of interest function from trees to general graphs.
Developed a technique to extract contextual subgraphs based on the adapted degree of interest metric.
Implemented and tested the approach on a large online database of legal citations.

Main Results:

The proposed interaction model enables efficient browsing of local graph contexts.
The adapted degree of interest function effectively extracts relevant subgraphs and manages visualization complexity.
The method proved effective in exploring a dense database of over 3 million legal citations.

Conclusions:

Remote browsing of immediate graph contexts is a viable and efficient alternative to global graph overviews.
The adapted degree of interest function provides a powerful mechanism for focused graph exploration.
This approach enhances user experience and data accessibility for large graph databases.