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 Experiment Videos

Clustering algorithm for determining community structure in large networks.

Josep M Pujol1, Javier Béjar, Jordi Delgado

  • 1Software Department, Technical University of Catalonia, Jordi Girona 1-3 A0-S106, 08034 Barcelona, Spain. jmpujol@lsi.upc.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 16, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Functional brain abnormalities in post COVID-19 condition and their relationship with cognition.

Scientific reports·2025
Same author

Can Personality Traits Affect Sleep Quality in Post-COVID-19 Patients?

Journal of clinical medicine·2025
Same author

Structural brain changes in post-COVID condition and its relationship with cognitive impairment.

Brain communications·2025
Same author

Cognition and objective sleep quality in post-COVID-19 patients.

Frontiers in psychology·2025
Same author

Retinal Microvasculature Changes Linked to Executive Function Impairment after COVID-19.

Journal of clinical medicine·2024
Same author

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19.

European archives of psychiatry and clinical neuroscience·2024
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

We developed a new algorithm for community detection in complex networks. It is more efficient and accurate than existing methods, making it ideal for analyzing large networks.

Area of Science:

  • Network Science
  • Computer Science
  • Data Analysis

Background:

  • Complex networks are ubiquitous in nature and technology.
  • Identifying community structures is crucial for understanding network behavior.
  • Existing algorithms face challenges with large-scale network analysis.

Purpose of the Study:

  • To develop a novel algorithm for efficient and accurate community detection in complex networks.
  • To compare the proposed algorithm against state-of-the-art methods, particularly Newman's fast algorithm.
  • To establish the suitability of the algorithm for analyzing medium to large-scale networks.

Main Methods:

  • Combining spectral analysis with modularity optimization.
  • Implementing and evaluating the proposed algorithm on complex network datasets.

Related Experiment Videos

  • Benchmarking against Newman's fast algorithm for efficiency and clustering accuracy (modularity).

    • Main Results:

    • The proposed algorithm achieves clustering accuracy comparable to leading modularity optimization methods.
    • The algorithm demonstrates superior efficiency compared to Newman's fast algorithm.
    • The algorithm outperforms Newman's fast algorithm in both speed and modularity-based accuracy.

    Conclusions:

    • The proposed algorithm offers a significant advancement in community detection for complex networks.
    • Its efficiency and accuracy make it a strong candidate for analyzing large networks (tens to hundreds of thousands of vertices).
    • This method provides a valuable tool for network scientists and data analysts.