Jove
Visualize
Contact Us

Related Concept Videos

Geometric Mean01:15

Geometric Mean

3.9K
The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
In cases of multiplicative data, the geometric mean is used for statistical analysis. First, the product of all the elements is taken. Then, if there are n elements in the...
3.9K
Ogive Graph01:07

Ogive Graph

6.7K
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...
6.7K
Graphing Antiderivatives01:30

Graphing Antiderivatives

52
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
52
Bar Graph01:07

Bar Graph

21.5K
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...
21.5K
Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

9.0K
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...
9.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

XYZ Integrability the Easy Way.

Journal of statistical physics·2026
Same journal

How Long does it Take to Train an Elephant Random Walk.

Journal of statistical physics·2026
Same journal

Matrix-Product State Skeletons in Onsager-Integrable Quantum Chains.

Journal of statistical physics·2026
Same journal

Ground State Energy Fluctuations of Pinned Elastic Manifolds.

Journal of statistical physics·2026
Same journal

Notes on the Jellinek-Berry Thermostated Ideal Gas.

Journal of statistical physics·2025
Same journal

Diffusion Properties of Small-Scale Fractional Transport Models.

Journal of statistical physics·2025
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 26, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

7.1K

Isolation and Connectivity in Random Geometric Graphs with Self-similar Intensity Measures.

Carl P Dettmann1

  • 1University of Bristol, Bristol, UK.

Journal of Statistical Physics
|April 19, 2019
PubMed
Summary
This summary is machine-generated.

Exploring random geometric graphs with diverse node distributions reveals that nonuniformity breaks Poisson properties but strengthens connectivity links. Fractal and finitely ramified distributions also impact graph connectivity.

Keywords:
ConnectivityDegree distributionFractalsRandom geometric graph

More Related Videos

A Behavioral Assay to Measure Responsiveness of Zebrafish to Changes in Light Intensities
09:39

A Behavioral Assay to Measure Responsiveness of Zebrafish to Changes in Light Intensities

Published on: October 3, 2008

17.1K
A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
08:23

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

6.7K

Related Experiment Videos

Last Updated: Jan 26, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

7.1K
A Behavioral Assay to Measure Responsiveness of Zebrafish to Changes in Light Intensities
09:39

A Behavioral Assay to Measure Responsiveness of Zebrafish to Changes in Light Intensities

Published on: October 3, 2008

17.1K
A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
08:23

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

6.7K

Area of Science:

  • Graph theory
  • Network science
  • Probability theory

Background:

  • Random geometric graphs (RGGs) model networks where nodes are points in space.
  • Connectivity in RGGs is often analyzed using uniform node distributions (e.g., on a square).
  • The Poisson distribution of isolated nodes and its relation to graph connectivity are key properties in the standard RGG model.

Purpose of the Study:

  • To investigate the properties of isolation and connectivity in RGGs with various self-similar node distributions.
  • To analyze the impact of nonuniform, fractal, and finitely ramified node distributions on graph properties.
  • To compare these properties with the standard uniform distribution model.

Main Methods:

  • Analysis of self-similar node distributions, including smooth, fractal, uniform, and nonuniform types.
  • Mathematical examination of node isolation and graph connectivity under different distribution models.
  • Evaluation of integral calculations and analytical arguments for both smooth and fractal distributions.

Main Results:

  • Nonuniform node distributions can disrupt the Poisson distribution of isolated nodes.
  • Nonuniformity enhances the correlation between isolated nodes and overall graph connectivity.
  • Connectivity transitions are broadened by nonuniform distributions, and finite ramification introduces further connectivity challenges.
  • Fractal distributions exhibit similar behaviors to smooth distributions, with some analytical differences.

Conclusions:

  • The choice of node distribution significantly influences the connectivity properties of random geometric graphs.
  • Nonuniformity and fractal structures present distinct challenges and behaviors compared to standard uniform models.
  • Understanding these variations is crucial for accurately modeling real-world networks with complex spatial distributions.