Knowledge Discovery from Growing Social Networks
Final rept. 13 Dec 2007-12 Dec 2009
SHIZUOKA UNIV (JAPAN)
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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.
- Information Science
- Sociology and Law
- Computer Systems