Activities information diffusion in Chinese largest recommendation social network: Patterns and generative model

Nowadays, networks play an indispensable role in social life, and social networks have become a new advertising medium for offline activities. Previous studies of information diffusion or behavior spread over social networks have mostly focused on diffusion models and analysis of virtual interaction between online users, and very few of them focus on the propagation of real world activities in these social networks. To address this problem, we use data obtained from the Chinese largest recommendation social network - Douban, and study how the offline activities spread from one user to another through Douban. By using cascading subgraphs and diffusion trees, we break a whole cascade into local subgraphs. After analyzing the activities of about 1.47 million users, we observe the statistical and topological characteristics of these local cascading subgraphs. Next, we find the size and degree distributions of these cascading subgraphs and several common patterns of topology of local cascades. Moreover, we also have some other interesting discoveries, like the relation between the number of initial adopters and the final cascade size, and the underlying influences driving user behaviors. Finally, we propose a diffusion model that can generate information cascades that follow the patterns we have observed, and validate it by empirical analysis.