Assessing the Quality of Diffusion Models Using Real-World Social Network Data

Recently, there has been growing interest in understanding information cascading phenomenon on popular social networks such as Face book, Twitter and Plurk. The numerous diffusion events indicate huge governmental and commercial potential. People have proposed several diffusion and cascading models based on certain assumption, but until now we do not know which one is better in predicting information propagation. In this paper, we propose a novel framework that utilizes the micro-blog data to evaluate which model is better under different circumstances. In our framework, we devise two schemes for evaluation: the direct and the indirect schemes. We conduct experiments using three diffusion models on Plurk data. The results show Independent Cascade model outperforms other diffusion models using direct scheme, while Linear Threshold model, Degree, In-Degree and Page Rank perform best using indirect scheme. The main contribution is to provide a general evaluation framework for various diffusion models.