Differential Information Diffusion Model in Social Network

A social network is modeled as a graph of nodes connected through interactions among users, an important medium for the information spreading and influence on users dynamically. Modeling information diffusion is still key problem to predict influences of information on users. In recent years, numerous literatures have proposed models to solve this problem. However, each of models is coming from different points of view. Based on differential equations and with a real mechanism of transferring, exchanging information in network, in this paper, it is proposed a model of temporal-spatial information diffusion, named differential information diffusion or DID model. This model is setup in accordance with topological structure of network, semantic content and interactive activities of users in network. Experimental computations show the feasibility of the proposed model, conformity with network topology and with prospects of scalability for large networks.