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Related Experiment Video

Updated: Apr 15, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Identification of core-periphery structure in networks.

Xiao Zhang¹, Travis Martin², M E J Newman^1,3

¹Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|April 15, 2015

Summary

This study introduces a new algorithm for network decomposition, identifying dense cores and sparse peripheries. The method efficiently reveals core-periphery structures in large networks, even when weak.

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Area of Science:

Network science
Statistical inference
Data analysis

Background:

Networks often exhibit a core-periphery structure.
Decomposing networks aids in understanding their organization and function.
Existing methods may struggle with weak structures.

Purpose of the Study:

To develop an algorithm for network decomposition into core and periphery.
To apply statistical inference for robust core-periphery identification.
To address limitations of existing community detection methods.

Main Methods:

Developed a generative model for core-periphery structure.
Employed expectation-maximization for parameter estimation.
Utilized belief propagation for network decomposition.

Main Results:

The algorithm efficiently decomposes large networks (millions of nodes).
Successfully identifies known core-periphery structures in benchmarks and real-world data.
Demonstrates robustness against the detectability transition.

Conclusions:

The proposed method offers an efficient and accurate approach to network decomposition.
Core-periphery structure is detectable regardless of its strength.
This algorithm advances the analysis of complex network organization.