Social network analysis provides innovative techniques to analyze interactions among entities by emphasizing social relationships. Diffusion in the social network can be referred to spread of information among interconnected nodes or entities in a network. The rate and intensity of diffusion depend upon network topology and initialization of network parameters. Individual nodes act as source of motivation for others in the diffusion process. The epidemic model is one of the basic diffusion models that helps in analyzing the transmission of infectious disease from one person to another through social connections. This can be further generalized for other socially connected platforms involving information exchange. In our research, we have proposed a diffusion methodology for tracking the rate with which information spread over underlying social interaction structure, with variation in time and other social parameters. In addition to forward state transitions, recoverable transition is also proposed, which allows a node currently under influence of incoming information, to revert back to previous state of perception. The proposed model also assists in predicting the fraction of population getting diffused over real and large-scale complex network for specific temporal domain.
Information diffusion modeling and analysis for socially interacting networks