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

Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
Coefficient of Correlation01:12

Coefficient of Correlation

6.1K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.1K
Network Covalent Solids02:18

Network Covalent Solids

13.5K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.5K
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

50.5K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
50.5K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K
Thermodynamics: Activity Coefficient01:24

Thermodynamics: Activity Coefficient

1.4K
Activity is the measure of the effective concentration of the species in solution. It can be expressed as the product of the molar concentration of the species and its activity coefficient. The activity coefficient is a dimensionless quantity and depends on the total ionic strength of the solution.
The activity coefficient is a measure of the deviation from ideal behavior. When the ionic strength of the solution is minimal, the activity coefficient of an ionic species is close to unity, making...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Price of information in games of chance: A statistical physics approach.

Physical review research·2024
Same author

Liquid Hopfield model: Retrieval and localization in multicomponent liquid mixtures.

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

Multicyclic Norias: A First-Transition Approach to Extreme Values of the Currents.

Journal of statistical physics·2024
Same author

Variational kinetic clustering of complex networks.

The Journal of chemical physics·2023
Same author

Instabilities of complex fluids with partially structured and partially random interactions.

Physical biology·2022
Same author

Modelling the interplay between the CD4<math> </math>/CD8<math> </math> T-cell ratio and the expression of MHC-I in tumours.

Journal of mathematical biology·2021

Related Experiment Video

Updated: Jun 29, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
00:07

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.4K

Clustering coefficients for networks with higher order interactions.

Gyeong-Gyun Ha1, Izaak Neri1, Alessia Annibale1

  • 1Department of Mathematics, King's College London, Strand, London WC2R 2LS, United Kingdom.

Chaos (Woodbury, N.Y.)
|April 1, 2024
PubMed
Summary

We introduce a quad clustering coefficient for hypergraphs. Real-world networks show highly clustered nodes, unlike random networks, indicating the need for higher-order interactions to identify complex network structures.

More Related Videos

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.3K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K

Related Experiment Videos

Last Updated: Jun 29, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
00:07

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.4K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.3K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K

Area of Science:

  • Network Science
  • Graph Theory
  • Complex Systems

Background:

  • Traditional clustering coefficients analyze pairwise interactions in graphs.
  • Hypergraphs, representing higher-order interactions, require specialized metrics.
  • Understanding network structure in systems with multi-way relationships is crucial.

Purpose of the Study:

  • Introduce a novel clustering coefficient for hypergraphs.
  • Quantify and analyze the quad clustering coefficient in real-world and random hypergraphs.
  • Investigate the characteristics of highly clustered nodes in complex networks.

Main Methods:

  • Definition of the quad clustering coefficient for directed and nondirected hypergraphs.
  • Calculation of the average quad clustering coefficient and its distribution.
  • Comparison with random hypergraphs generated via the configuration model.
  • Analysis of node degrees and hyperedge cardinalities for highly clustered nodes.

Main Results:

  • Real-world hypergraphs exhibit a significant proportion of nodes with maximal quad clustering coefficient values.
  • No such highly clustered nodes were found in random hypergraph models.
  • Highly clustered nodes in real networks can possess large degrees and high hyperedge cardinalities.
  • Pairwise clustering coefficients on projected graphs fail to identify these highly clustered nodes.

Conclusions:

  • The quad clustering coefficient effectively identifies nodes with high local clustering in hypergraphs.
  • Higher-order interactions are essential for capturing complex network structures missed by pairwise analysis.
  • Real-world networks possess distinct structural properties not replicated in simple random models.