Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise

Abstract

Community detection tasks have received a lot of attention across statistics, machine learning, and information theory with a large body of work concentrating on theoretical guarantees for the stochastic block model. One line of recent work has focused on modeling the spectral embedding of a network using Gaussian mixture models (GMMs) in scaling regimes where the ability to detect community memberships improves with the size of the network. However, these regimes are not very realistic. This paper provides tractable methodology motivated by new theoretical results for networks with non-vanishing noise. We present a procedure for community detection using GMMs that incorporates certain truncation and shrinkage effects that arise in the non-vanishing noise regime. We provide empirical validation of this new representation using both simulated and real-world data.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2019
Accession Number
AD1188606

Entities

People

  • Alexander Volfovsky
  • Galen Reeves
  • Heather Mathews
  • Vaishakhi Mayya

Organizations

  • Duke University

Tags

Communities of Interest

  • Advanced Electronics
  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Covariance
  • Data Analysis
  • Data Mining
  • Data Science
  • Eigenvalues
  • Eigenvectors
  • Embedding
  • Engineering
  • Information Processing
  • Information Science
  • Information Theory
  • Learning
  • Low Noise
  • Machine Learning
  • Military Research
  • Probability
  • Simulations
  • Social Networks
  • Social Sciences
  • Statistics
  • Truncation

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms