Learning to Predict Social Influence in Complex Networks

Abstract

In this project the following five major achievement are made. 1) Two kinds of information diffusion models incorporating asynchronous time delay and a method to select models that better explains the observation, 2) a method to learn and predict opinion share using a variant of voter model, 3) a method to detect changes in opinion share, 4) a method to detect changes in diffusion probability, and 5) a method to learn the strength of opinion. Each of them uses probabilistic models and machine learning techniques to learn the model parameters from the observation. These are important steps to construct basic methods for learning to predict social influence in complex networks. All of them have been published in international conferences and/or international journals. In total there are 17 publications and they are included in the final report.

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Document Details

Document Type
Technical Report
Publication Date
Mar 29, 2012
Accession Number
ADA559262

Entities

People

  • Kazumi Saitō

Organizations

  • University of Shizuoka

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Cyber
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Change Detection
  • Computational Science
  • Data Mining
  • Electronic Mail
  • Information Processing
  • Information Science
  • Information Systems
  • Knowledge Management
  • Machine Learning
  • Mathematical Models
  • Network Science
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Social Networks
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Economics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks