An Information Diffusion Model Based on Explosion Shock Wave Theory on Online Social Networks

Along with the growing popularity of online social networks, an environment has been set up with information spreading faster and wider than ever before, which has changed the way of information diffusion. Previous empirical research and propagation models have been conducted to illustrate how information propagates on online social networks. However, due to the complexity of information diffusion, there are still many important issues yet to be resolved. In order to tackle this problem, most studies have assumed that information is transmitted along the edges on online social networks, while most research goals aim to discover nodes that have been affected by information diffusion. However, we found that processes of information diffusion on online social networks vary from one another; some topics such as people's livelihood and education are long-acting while some entertainment news is short-lived. The scale of propagation may be similar in the end, but the spreading process would be completely different. With the purpose of modeling the propagation process more realistically, we propose a novel model, the Information Diffusion Model, based on Explosion Shock Wave Theory. The Information Diffusion Model compares the propagation process to the explosion of an information bomb at the source, with the information shock waves progressively spread from near to far. Additionally, we establish rules of information transmission between a pair of individuals. The approach we adopted demonstrates four strengths. First, it models information diffusion on OSNs considering the differences between individuals and individual social behaviors, which takes the individual background knowledge and forgetting factors into account. Second, it holds the point that the attractiveness of information to individuals is related to the value of information. Third, it recognizes the role of community in the diffusion process; with a higher sense of trust established in a community, the spread of information would be more convenient. More importantly, the model we put forth is applicable to different types of real online social network datasets. Many experiments with different settings and specifications are conducted to verify the advantages of the model, and the results obtained are very promising.