Effects of network structures and fermi function's parameter β in promoting information spreading on dynamic social networks

Network represents a multitude of interactions through which information spreads within a society. Indeed, people are connected according to the way they interact with one another and the resulting network significantly determines the efficiency and speed of information spreading. This paper aimed at examining how topological structures of dynamic social network ks and Fermi function’s parameter β influence information spreading. In order to carry out this study preciously, two models were proposed to generate a variety of network structures. To study the spreading process, the models were integrated with an epidemic Susceptible-Infected-Recovered (SIR) model and designed in such a way that nodes rewire network edges according to Fermi function which depends on a parameter β. By studying the number of recovered nodes generated in the spreading process and the number of acquainted nodes that are receiving information in each time step, the results suggested that network structure and both positive and negative β play an important role in promoting information spreading. These results give a good indication that the structure of a society influences the spreading process. More specifically, the structure of dynamic interactions is a good promoter of information spreading. Moreover, it is proposed that rewiring more than three edges of random network could yield no significant advantages in promoting information spreading. The present study likely enriches our knowledge and provides more insight on information spreading.