Jove
Visualize
Contact Us
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 Concept Videos

What are Populations and Communities?00:30

What are Populations and Communities?

38.1K
Overview
38.1K
Ogive Graph01:07

Ogive Graph

6.9K
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...
6.9K
Graphing Antiderivatives01:30

Graphing Antiderivatives

79
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...
79
Graphs of Functions01:30

Graphs of Functions

365
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...
365
Bar Graph01:07

Bar Graph

23.3K
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...
23.3K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

435
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
435

You might also read

Related Articles

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

Sort by
Same author

Network evolution of centrality maximizing agents: An empirical evidence of nestedness emergence.

PNAS nexus·2025
Same author

Extended yard sale model of wealth distribution on Erdős-Rényi random networks.

Physical review. E·2025
Same author

Analysis of the inference of ratings and rankings in complex networks using discrete exterior calculus on higher-order networks.

Physical review. E·2025
Same author

A Data Engineering Framework for Ethereum Beacon Chain Rewards: From Data Collection to Decentralization Metrics.

Scientific data·2025
Same author

Productive scientists are associated with lower disruption in scientific publishing.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Statistical detection of selfish mining in proof-of-work blockchain systems.

Scientific reports·2024
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Feb 15, 2026

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

7.3K

Hierarchical benchmark graphs for testing community detection algorithms.

Zhao Yang1, Juan I Perotti2,3, Claudio J Tessone1,2

  • 1URPP Social Networks, University of Zurich, Andreasstrasse 15, CH-8050 Zürich, Switzerland.

Physical Review. E
|January 20, 2018
PubMed
Summary
This summary is machine-generated.

Researchers developed a new benchmark for testing community detection algorithms in complex systems. This Ravasz-Barabási-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark effectively evaluates hierarchical network structures.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold
05:28

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Published on: February 10, 2023

2.2K

Related Experiment Videos

Last Updated: Feb 15, 2026

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

7.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold
05:28

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Published on: February 10, 2023

2.2K

Area of Science:

  • Complex Systems Science
  • Network Science
  • Data Mining

Background:

  • Hierarchical organization is a key feature of complex systems across social, biological, and technical domains.
  • Understanding these hierarchies requires analyzing the underlying interaction networks.
  • Existing benchmarks do not adequately test hierarchical community structure detection.

Purpose of the Study:

  • To introduce a novel benchmark graph for testing hierarchical community detection algorithms.
  • To evaluate the performance of popular community detection methods on hierarchical structures.

Main Methods:

  • Extended the Lancichinetti-Fortunato-Radicchi (LFR) benchmark graph ensemble.
  • Incorporated the Ravasz-Barabási hierarchical network construction rules.
  • Tested three community detection algorithms using traditional and hierarchical mutual information metrics.

Main Results:

  • The developed Ravasz-Barabási-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark successfully generates complex hierarchical structures.
  • The benchmark presents a significant challenge for existing community detection algorithms.
  • Performance variations among algorithms were quantified using mutual information metrics.

Conclusions:

  • The RB-LFR benchmark is a valuable tool for assessing community detection algorithms in hierarchical networks.
  • This benchmark fills a critical gap in evaluating methods for complex system analysis.
  • Further research can utilize this benchmark to develop more robust community detection techniques.