Infectious communities forging: Using information diffusion model in social network mining

Abstract

This article proposes a new model for clustering individual nodes based on node's interrelation with a real-life mining application. The model is capable of detecting a network topology based on information flow and therefore could be easily extended and applied in a variety of today's research fields. E.g. discover audience group sharing similar attitude, or retrieve authors' academic referencing group or plot active friend society in social networks. An effective algorithm: Boundary Growth Algorithm is proposed through which people can find the underlying structure of networks. Extensive experimental evaluations demonstrate the effectiveness of our approach. © 2011 Springer-Verlag.