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

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

You might also read

Related Articles

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

Sort by
Same author

Antibacterial mechanisms of magnolol against Streptococcus agalactiae and immunomodulatory effects in Schizothorax prenanti.

Fish & shellfish immunology·2026
Same author

Pressure-Induced Crossover from Antiferromagnetism to Single-Band Superconductivity via Multiband Superconductivity in Quasi-1D CrZr<sub>4</sub>Te<sub>14</sub>.

Journal of the American Chemical Society·2026
Same author

Flooded cultivation improves grain yield and appearance quality while reducing nutritional quality in Shanlan upland rice (Oryza sativa L.).

Food chemistry·2026
Same author

Mechanistic dissection of SMARCA2/4 molecular glues reveals programmable switching between DCAF16 and FBXO22.

Cell chemical biology·2026
Same author

Discovery of Serum Exosomal Protein Biomarkers for Early- and Late-Stage Lung Cancer Through Comparative Proteomic Analysis.

Anti-cancer agents in medicinal chemistry·2026
Same author

The effects of hypoxia environment at high-altitudes on inflammation and angiogenesis in wound healing process:A meta-analysis and review.

Journal of tissue viability·2026

Related Experiment Video

Updated: Jan 16, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Enhanced Key Node Identification in Complex Networks Based on Fractal Dimension and Entropy-Driven Spring Model.

Zhaoliang Zhou1, Xiaoli Huang1,2, Zhaoyan Li3

  • 1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

Identifying key nodes in complex networks is challenging. The Second-Order Neighborhood Entropy Fuzzy Local Dimension Spring Model (SNEFLD-SM) improves critical node detection accuracy by integrating multiple centrality measures and information entropy.

Keywords:
attenuation factorcomplex networksfractal technologyidentify key nodeinformation entropynode influence rangespring model

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.6K

Related Experiment Videos

Last Updated: Jan 16, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.6K

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Computational Graph Theory

Background:

  • Identifying critical nodes is crucial for understanding and managing complex networks.
  • Traditional centrality methods often rely on limited local or global network information.
  • Existing approaches may struggle with multi-scale networks and the "rich-club" phenomenon.

Purpose of the Study:

  • To propose a novel model for accurately identifying key nodes in complex networks.
  • To overcome limitations of traditional centrality methods by incorporating diverse network properties.
  • To enhance the robustness and efficiency of critical node detection.

Main Methods:

  • Developed the Second-Order Neighborhood Entropy Fuzzy Local Dimension Spring Model (SNEFLD-SM).
  • Integrated second-order neighborhood centrality, betweenness centrality, and fractal dimension within a spring model framework.
  • Incorporated information entropy and node influence range, with an attenuation factor to mitigate the "rich-club" effect.

Main Results:

  • SNEFLD-SM demonstrated higher accuracy in critical node detection compared to traditional methods across six test networks.
  • The model effectively captures network self-similarity and hierarchical structures using fractal technology.
  • Information entropy enhanced the model's ability to distinguish node importance and reduced computational costs.

Conclusions:

  • SNEFLD-SM offers a more accurate and comprehensive approach to identifying key nodes in complex networks.
  • The integration of fractal dimensions and information entropy provides superior analysis of multi-scale network properties.
  • The model's ability to suppress the "rich-club" phenomenon improves its applicability to diverse network structures.