Jove
Visualize
Contact Us

Related Concept Videos

Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

261
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
261
Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

641
The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...
641
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
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...
16.1K
Network Function of a Circuit01:25

Network Function of a Circuit

660
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
660

You might also read

Related Articles

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

Sort by
Same author

Age polyethism can emerge from social learning: A game-theoretic investigation.

PLoS computational biology·2025
Same author

Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning.

Cyborg and bionic systems (Washington, D.C.)·2025
Same author

Social Learning versus Individual Learning in the Division of Labour.

Biology·2023
Same author

Explaining workers' inactivity in social colonies from first principles.

Journal of the Royal Society, Interface·2023
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
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 20, 2026

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

4.8K

SimNet: Similarity-based network embeddings with mean commute time.

Moein Khajehnejad1

  • 1Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, Germany.

Plos One
|August 16, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for learning node embeddings in weighted networks using mean commute time (MCT) for improved similarity modeling. The approach enhances network analysis tasks like classification and link prediction.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
10:53

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Published on: January 3, 2017

10.3K

Related Experiment Videos

Last Updated: Jan 20, 2026

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

4.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
10:53

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Published on: January 3, 2017

10.3K

Area of Science:

  • Network Science
  • Machine Learning
  • Graph Theory

Background:

  • Node embeddings are crucial for network analysis.
  • Existing methods may not fully capture complex network structures.
  • Weighted undirected networks require specialized embedding techniques.

Purpose of the Study:

  • To propose a new approach for learning node embeddings in weighted undirected networks.
  • To leverage mean commute time (MCT) for enhanced similarity modeling.
  • To improve the accuracy of network analysis tasks.

Main Methods:

  • Performing random walks on the network to extract structural information.
  • Utilizing a similarity-based framework for node embedding learning.
  • Defining a novel similarity matrix based on pair-wise mean commute time (MCT).
  • Calculating proximity using the pseudoinverse of the graph's Laplacian matrix.

Main Results:

  • The proposed method effectively captures node proximity using MCT.
  • The novel similarity matrix adequately represents connections between similar nodes.
  • Experiments on real-world networks show superior performance in classification, clustering, visualization, and link prediction.

Conclusions:

  • Mean commute time (MCT) is a crucial metric for quantifying node proximity.
  • The proposed node embedding method offers significant improvements over existing approaches.
  • This work provides a more adequate representation of network structures for various analytical tasks.