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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.6K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.6K
Ogive Graph01:07

Ogive Graph

5.7K
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...
5.7K
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

784
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
784
SFG Algebra01:16

SFG Algebra

148
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
148
Multiple Bar Graph01:07

Multiple Bar Graph

5.4K
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...
5.4K
pV-Diagrams01:18

pV-Diagrams

4.3K
The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Logarithmic kinetics and bundling in random packings of elongated 3D physical links.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Switchover phenomenon induced by epidemic seeding on geometric networks.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

Multifractal network generator.

Proceedings of the National Academy of Sciences of the United States of America·2010
Same author

A novel quantitative method for measuring obstruction in the upper urinary tract: the 'obstruction coefficient'.

International journal of urology : official journal of the Japanese Urological Association·2008
Same journal

Ubiquity of graphs with nowhere-linear end structure.

Journal of graph theory·2024
Same journal

Hamiltonian decompositions of 4-regular Cayley graphs of infinite abelian groups.

Journal of graph theory·2022
Same journal

Occupancy fraction, fractional colouring, and triangle fraction.

Journal of graph theory·2021
Same journal

Single-conflict colouring.

Journal of graph theory·2021
Same journal

Hamiltonian cycles in planar cubic graphs with facial 2-factors, and a new partial solution of Barnette's Conjecture.

Journal of graph theory·2020
Same journal

On dihedral flows in embedded graphs.

Journal of graph theory·2019
See all related articles

Related Experiment Video

Updated: Aug 2, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K

Locally common graphs.

Endre Csóka1, Tamás Hubai1,2, László Lovász1,2

  • 1Combinatorics and Applications Research Division Alfréd Rényi Institute of Mathematics Budapest Hungary.

Journal of Graph Theory
|April 17, 2023
PubMed
Summary
This summary is machine-generated.

Researchers explored common graphs, proving that graphs containing K4 are not locally common but are weakly locally common. This advances understanding of graph properties and common graph characterization.

Keywords:
graph homomorphismsgraph theory

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

382

Related Experiment Videos

Last Updated: Aug 2, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

382

Area of Science:

  • Graph theory
  • Combinatorics
  • Theoretical computer science

Background:

  • Goodman's theorem established a lower bound for triangle counts in graphs and their complements.
  • Erdős conjectured a similar inequality for K4, which was disproven by Thomason.
  • Common graphs satisfy an analogous inequality, but their characterization remains elusive.

Purpose of the Study:

  • To investigate two versions of locally common graphs using graph limits.
  • To refine understanding of common graph properties, particularly concerning K4.
  • To explore the conditions under which graphs are considered locally or weakly locally common.

Main Methods:

  • Utilizing the framework of graph limits to analyze graph properties.
  • Applying techniques from extremal graph theory.
  • Developing new criteria for local and weak local commonality.

Main Results:

  • Demonstrated that no graph containing K4 can be locally common.
  • Proved that all graphs containing K4 are weakly locally common.
  • Established that not all connected graphs possess weak local commonality.

Conclusions:

  • The study provides a nuanced understanding of local and weak local commonality in graphs.
  • Results offer insights into the structure of common graphs and their limitations.
  • Further research is needed to fully characterize common graphs.