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MODEL-BASED CLUSTERING OF LARGE NETWORKS.

Duy Q Vu1, David R Hunter2, Michael Schweinberger3

  • 1Department of Mathematics and Statistics University of Melbourne Parkville, Victoria 3010 Australia duy.vu@unimelb.edu.au.

The Annals of Applied Statistics
|November 26, 2015
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Summary
This summary is machine-generated.

This study introduces a flexible network clustering framework using finite mixture models for large, discrete networks. The novel approach improves estimation algorithms and standard error calculation, enabling analysis of massive datasets.

Keywords:
EM algorithmsMM algorithmsSocial networksfinite mixture modelsgeneralized EM algorithmsstochastic block modelsvariational EM algorithms

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Area of Science:

  • Computational statistics
  • Network analysis
  • Machine learning

Background:

  • Network clustering is crucial for understanding complex systems.
  • Existing methods struggle with large-scale, discrete-valued networks.
  • Finite mixture models offer a probabilistic approach to clustering.

Purpose of the Study:

  • To develop a scalable and flexible network clustering framework.
  • To enhance existing model-based clustering algorithms for networks.
  • To enable the analysis of very large discrete-valued networks.

Main Methods:

  • Finite mixture models with novel parameterizations.
  • Variational generalized Expectation-Maximization (EM) algorithms with minorization-maximization (MM).
  • Parametric bootstrap for standard error estimation using Monte Carlo simulations.

Main Results:

  • A flexible framework for discrete-valued network clustering.
  • Improved estimation algorithm for large networks.
  • Successful application to a network with over 131,000 nodes and 17 billion edges.

Conclusions:

  • The proposed framework effectively clusters large, discrete-valued networks.
  • The enhanced methods offer improved flexibility and scalability.
  • This work advances model-based network analysis for massive datasets.