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  1. Home
  3. A Resampling-based Framework For Network Structure Learning In High-dimensional Data.
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  3. A Resampling-based Framework For Network Structure Learning In High-dimensional Data.

Related Experiment Video

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

A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data.

Ziwei Huang1,2, Zeyuan Song3,4, Paola Sebastiani3,4,5

  • 1Department of Physics, Boston University, Boston, MA.

Arxiv
|May 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

RSNet is a new R package for robust network inference, addressing small sample sizes in high-dimensional data. It offers interpretable analysis of complex networks using graphlet-based methods.

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Area of Science:

  • Computational biology
  • Network science
  • Statistical inference

Background:

  • High-dimensional data analysis often faces challenges with limited sample sizes.
  • Robust and interpretable network inference is crucial for understanding complex biological systems.
  • Existing methods may struggle with mixed data types and higher-order network structures.

Purpose of the Study:

  • To introduce RSNet, an open-source R package for resampling-based network inference.
  • To provide a framework for robust and interpretable network analysis in high-dimensional settings.
  • To address challenges associated with limited sample sizes and mixed data types.

Main Methods:

  • Developed a resampling-based framework supporting bootstrap, subsampling, and cluster-based strategies.
  • Implemented estimation for partial correlation networks (Gaussian networks) and conditional Gaussian Bayesian networks.
  • Integrated graphlet-based topology analysis for higher-order connectivity and edge sign insights.
  • Enabled efficient construction of signed graphlet degree vector matrices (GDVMs) for sparse networks.

    • Main Results:

    • RSNet provides statistically reliable network inference for high-dimensional data.
    • The package supports mixed data types (continuous and discrete variables).
    • Efficient GDVM construction allows scalable analysis of higher-order network structures.
    • Graphlet analysis enhances interpretability at single-node and subnetwork levels.

    Conclusions:

    • RSNet offers a versatile and statistically sound tool for network inference.
    • The package enhances interpretability through graphlet-based topology analysis.
    • RSNet effectively addresses limited sample size challenges in high-dimensional data analysis.