A Comparison of Four Information Diffusion Inspired-Based Models According to Real Data Diffusion Similarity

The rapid revolution in web technology has brought much attention to social media platforms. The main focus was on how information spreads across the network topology. Various information diffusion models have been introduced to study diffusion on social media. However, the inspired-based model is a spark that can more realistically mimic real-world scenarios. In this study, we compared four inspired-based models to flash a glance at (immune-inspired, genetic-based, potential-driven, and particle collision) models in terms of their strengths and limitations. We compared the previously listed models based on their similarity to actual data diffusion. Then, we propose an experiment on these models to show how the Immune-based model is the best for fitting the real data propagation of the introduced models, and how the seed set of the nodes is the primary factor that determines the diffusion paths.

Keywords: Social Networks, Information Diffusion, System-Inspired.