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NETWORK MODELLING OF TOPOLOGICAL DOMAINS USING HI-C DATA.

The annals of applied statistics·2020
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Related Experiment Video

Updated: Oct 15, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Two provably consistent divide-and-conquer clustering algorithms for large networks.

Soumendu Sundar Mukherjee1,2, Purnamrita Sarkar3, Peter J Bickel4

  • 1Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata 700108, India; soumendu041@gmail.com bickel@stat.berkeley.edu.

Proceedings of the National Academy of Sciences of the United States of America
|October 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces divide-and-conquer algorithms for network community detection. These methods efficiently scale traditional algorithms to large networks, reducing computational cost and maintaining accuracy.

Keywords:
clusteringdivide-and-conquernetworks

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Area of Science:

  • Network science
  • Computer science
  • Data mining

Background:

  • Community detection is crucial for understanding network structure.
  • Traditional algorithms face scalability challenges with large networks.
  • Existing methods like spectral clustering are computationally intensive.

Purpose of the Study:

  • To develop efficient and scalable algorithms for community detection.
  • To reduce the computational cost of existing community detection methods.
  • To maintain or improve accuracy in large-scale network analysis.

Main Methods:

  • Proposing two novel divide-and-conquer algorithms.
  • Performing clustering on smaller subgraphs.
  • Combining subgraph results into a global clustering.

Main Results:

  • Significantly reduced computational cost compared to traditional methods.
  • Maintained or improved accuracy in community detection.
  • Algorithms demonstrated parallelizability for enhanced performance.

Conclusions:

  • Divide-and-conquer strategies offer an effective approach for scaling community detection.
  • These methods provide a versatile solution for analyzing large, complex networks.
  • The proposed algorithms are consistent and validated through simulations and real-world data.