A novel information cascade model in online social networks

Abstract

The spread and diffusion of information has become one of the hot issues in today’s social network analysis. To analyze the spread of online social network information and the attribute of cascade, in this paper, we discuss the spread of two kinds of users’ decisions for city-wide activities, namely the “want to take part in the activity” and “be interested in the activity”, based on the users’ attention in “DouBan” and the data of the city-wide activities. We analyze the characteristics of the activity-decision’s spread in these aspects: the scale and scope of the cascade subgraph, the structure characteristic of the cascade subgraph, the topological attribute of spread tree, and the occurrence frequency of cascade subgraph. On this basis, we propose a new information spread model. Based on the classical independent diffusion model, we introduce three mechanisms, equal probability, similarity of nodes, and popularity of nodes, which can generate and affect the spread of information. Besides, by conducting the experiments in six different kinds of network data set, we compare the effects of three mechanisms above mentioned, totally six specific factors, on the spread of information, and put forward that the node’s popularity plays an important role in the information spread.