## 2 Related Work  
### Information Diffusion Models  
With the growing popularity of OSNs, several problems related to it have attracted the attention of researchers. Among these problems, the foundation is to build an information diffusion model that is effectively close to the real situation [3]. Based on a careful classification on the designing conceptions of the existing work, we have summarized two groups: macroscopic and microscopic approaches. Macroscopic models divide the nodes into different categories (that is, states), focusing on the state evolution of the nodes in each category. An epidemic model is the most common model used to study information propagation from a macro perspective, in which the spreading is similar to that of a disease in a community. [PERSON] and [PERSON] [4] proposed the classic Daley-Kendall model by studying the similarities between the spread of infectious diseases and the diffusion of information. Later, variants of this classic model were developed, such as susceptible-infected-recovered (SIR) [5], susceptible-infected-susceptible (SIS) [6], susceptible-infected-recovered-susceptible (SIRS) [7], susceptible-exposed-infectious-recovered (SEIR) [8], and other similar epidemic models. Furthermore, researchers have begun to consider the impact of human behaviors and different mechanisms on the process of information diffusion. [PERSON] et al. [5] introduced the trust mechanism between people into the SIR model, and proved through experiments that the trust mechanism can reduce the final size of rumor spreading. Considering the incomplete reading behavior of users during the diffusion process, [PERSON] et al. [9] proposed an improved model called microblog-susceptible-infected-removed (Mb-SIR) for information diffusion. Experimental results prove that this model is more accurate in describing the information spread in Weibo. To incorporate the forgetting and remembering mechanism, [PERSON] et al. [10; 11] considered forgetting and memory factors in the diffusion process and proposed the susceptible-infected-hibernator-removed (SIHR) model as an extension of the classical SIR model; [PERSON] et al. [12] analyzed the impact of social reinforcement on the spreading rate. They proposed a rumor diffusion model based on social reinforcements. [PERSON] et al. [13] considered the hesitating mechanism. [PERSON] et al. [14] proposed a competitive information susceptible-infected-recovered (CISIR) model for studying the competitive information diffusion process on online social networks. The most frequently used microscopic models are the independent cascades (IC) model and the linear threshold (LT) model [15; 16]. The IC and LT models are the milestones in studying the issue of information diffusion. The IC model [16] assumes that each node has the ability to activate its neighbors based on probability. Each active node has only one chance to activate the inactive nodes among the neighbors. If a node has already activated one of its neighbors, it then has no further chance to activate others. The LT model [15] assumes that every node has a randomly selected threshold between 0 and 1. In addition, the node will be affected by its neighbor in consideration of a given weight. Given a set of initial active nodes and randomly selected thresholds, if the number of active neighbors of a node exceeds its private threshold, the node will be activated. In order to improve the IC and LT models, many researchers have proposed different algorithms. [PERSON] et al. [17] proposed an IC model with meeting events (IC-M) based on the classic IC model. In IC-M model, activated nodes have a probability to meet the inactive nodes. Compared with the common IC model, this model is closer to the actual situation. [PERSON] et al. [18] studied the notion of competing campaigns on social networks and proposed a multicampaign IC model to simulate the diffusion of multiple pieces of information at the same time. Considering the time dependence of information, [PERSON] et al.