Empirical paths to the spread of information in location-based social networks

Spreading phenomena in complex networks have attracted much attention in recent years. However, most of the previous works only concern the critical thresholds and final states of the spread. In this paper, we investigate the empirical spreading paths in real location-based networks and find an abnormal phenomenon that the transferring probability of an epidemic between users varies with time, which violates the classical spreading models with a constant transferring probability. Besides, we observe an interesting delay gap between the maximal spreading velocity and the maximal transferring probability, where the spreading velocity refers to the fraction of newly infected nodes, and transferring probability represents the probability that a susceptible individual gets infected by one of its infected neighbors. Then we propose an advanced SI (susceptible-infected) model to analyze the problem, which could analytically explain the delay gap between the spreading velocity and the transferring probability. Experiments in BA and ER model networks demonstrate the effectiveness of our model. Thus, our work provides a deep understanding of the dynamics of the spreading problems.