Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model

Abstract

Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicative Attribute Graph (MAG) model that considers nodes with categorical attributes and models the probability of an edge as the product of individual attribute link formation affinities. We develop a scalable variational expectation maximization parameter estimation method. Experiments show that MAG model reliably captures network connectivity as well as provides insights into how different attributes shape the network structure.

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Document Details

Document Type
Technical Report
Publication Date
Jun 24, 2011
Accession Number
ADA613151

Entities

People

  • Jure Leskoved
  • Myunghwan Kim

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bernoulli Distribution
  • Boundaries
  • Computations
  • Foreign Languages
  • Iterations
  • Mathematical Analysis
  • Maximum Likelihood Estimation
  • Probability
  • Random Variables
  • Social Media
  • Social Networking Services
  • Social Networks
  • Statistics
  • Structural Properties
  • Visual Inspection

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Graph Algorithms and Convex Optimization.
  • Regression Analysis.