Accession Number:

ADA512875

Title:

Knowledge Discovery from Growing Social Networks

Descriptive Note:

Final rept. 13 Dec 2007-12 Dec 2009

Corporate Author:

SHIZUOKA UNIV (JAPAN)

Personal Author(s):

Report Date:

2009-12-24

Pagination or Media Count:

155.0

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 susceptibleinfectedsusceptible SIS model. 6 A new method for learning continuous-time information diffusion model for social behavioral data analysis.

Subject Categories:

  • Information Science
  • Sociology and Law
  • Computer Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE