Bayesian Mixed-Membership Models of Complex and Evolving Networks

Abstract

This thesis provides a methodological framework for the statistical analysis of complex graphs and dynamic networks.1 In it, I develop probabilistic algorithms that generate, evolve and integrate a heterogeneous collection of graphs, I study the statistical models these algorithms implicitly specify, and I develop strategies for estimating the set of quantities on which they depend in the context of applications to social and biological networks.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2006
Accession Number
ADA488405

Entities

People

  • Edoardo Airoldi

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Cognitive Science
  • Computational Science
  • Data Mining
  • Data Science
  • Databases
  • Information Processing
  • Information Retrieval
  • Information Science
  • Knowledge Management
  • Machine Learning
  • Monte Carlo Method
  • Network Science
  • Probabilistic Models
  • Surveys

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

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