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

Systematic identification of statistically significant network measures.

Etay Ziv1, Robin Koytcheff, Manuel Middendorf

  • 1College of Physicians and Surgeons, Columbia University, New York, New York 10027, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 9, 2005
PubMed
Summary

This study introduces a novel graph embedding space for network statistical analysis, improving motif hub discovery and computational efficiency for weighted and signed graphs. The method enhances network randomization and significance testing for better classification.

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

Hierarchical classification of immune cell transcriptomes at population-scale.

bioRxiv : the preprint server for biology·2026
Same author

Quantitative analysis of lung microwave ablation zone volume and shape.

European radiology experimental·2026
Same author

T-Cell Clonal Expansion in Peripheral Blood Following Interventional Radiology Procedures for Metastatic Liver Cancer.

Cancers·2026
Same author

Yttrium-90 Radioembolization Versus Transarterial Embolization for Lung Carcinoid Hepatic Metastasis.

JTO clinical and research reports·2026
Same author

Automated Deformable Registration and Three-dimensional Margin Assessment for Predicting Local Recurrence after Lung Thermal Ablation.

Radiology. Imaging cancer·2026
Same author

Changes in Procedure Times for Interventional Radiology Attendings Post-fellowship: What are the Learning Curves for Interventional Radiology Procedures?

Academic radiology·2026

Area of Science:

  • Graph theory
  • Network science
  • Computational statistics

Background:

  • Statistical analysis of networks is crucial for understanding complex systems.
  • Existing methods for network analysis often lack efficiency and flexibility.
  • Identifying significant network structures, such as motif hubs, remains a challenge.

Purpose of the Study:

  • To present a novel graph embedding space for network statistical analysis.
  • To improve the discovery of significant subgraph patterns (motif hubs).
  • To enhance computational efficiency and generalizability to various graph types.

Main Methods:

  • Developed a graph embedding space using global scalars derived from the adjacency matrix.
  • Introduced improved network randomization and significance testing, learning distributions instead of assuming Gaussianity.

Related Experiment Videos

  • The method is generalizable to weighted and signed graphs.

    • Main Results:

    • The proposed method enables the discovery of multiple overlapping significant subgraphs (motif hubs).
    • Achieved computational efficiency compared to subgraph census methods.
    • Demonstrated flexibility and generalizability to weighted and signed networks.

    Conclusions:

    • The graph embedding space provides a systematic approach for network statistical analysis.
    • The method facilitates the identification of significant network scalars.
    • Suggests potential for machine-learning techniques in network classification.