Knowledge Discovery from Growing Social Networks

Abstract

The project explored mathematical models to explain, control and visualize a wide variety of information diffusion processes. The main results are the following six: 1) A very efficient method for minimizing the propagation of undesirable things by blocking a limited number of links in a network. 2) An effective visualization method for understanding a complex network, in particular its dynamical aspect such as information diffusion process. 3) A new scheme for empirical study to explore the behavioral characteristics of representative information diffusion models. 4) An effective method for ranking influential nodes in complex social networks by estimating diffusion probabilities from observed information diffusion data using the popular independent cascade (IC) model. 5) A very efficient method for discovering the influential nodes in a social network under the susceptible/infected/susceptible (SIS) model. 6) A new method for learning continuous-time information diffusion model for social behavioral data analysis.

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Document Details

Document Type
Technical Report
Publication Date
Dec 24, 2009
Accession Number
ADA512875

Entities

People

  • Kazumi Saitō

Organizations

  • University of Shizuoka

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Computational Science
  • Computer Networks
  • Data Analysis
  • Data Mining
  • Generative Models
  • Heuristic Methods
  • Machine Learning
  • Mathematical Models
  • Network Science
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Social Networks
  • Two Dimensional

Fields of Study

  • Biology
  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.
  • Systems Analysis and Design