Accelerating information diffusion in social networks under the Susceptible-Infected-Susceptible epidemic model

Abstract

Standard Susceptible-Infected-Susceptible (SIS) epidemic models assume that a message spreads from the infected to the susceptible nodes due to only susceptible-infected epidemic contact. We modify the standard SIS epidemic model to include direct recruitment of susceptible individuals to the infected class at a constant rate (independent of epidemic contacts), to accelerate information spreading in a social network. Such recruitment can be carried out by placing advertisements in the media. We provide a closed form analytical solution for system evolution in the proposed model and use it to study campaigning in two different scenarios. In the first, the net cost function is a linear combination of the reward due to extent of information diffusion and the cost due to application of control. In the second, the campaign budget is fixed. Results reveal the effectiveness of the proposed system in accelerating and improving the extent of information diffusion. Our work is useful for devising effective strategies for product marketing and political/social-awareness/crowd-funding campaigns that target individuals in a social network.

Public Opinion Spread and Guidance Strategy under COVID-19: A SIS Model Analysis

“…Suo and Guo found that the old nodes propagate faster than the new nodes, and that the initial node only affects the initial stage of propagation, not the entire propagation stage [24]. Kandhway and Kuri introduced susceptible individuals based on the SIS model, which provides a new strategy for improving the effectiveness of policy publicity and product marketing [25]. Gong et al found that the super network is more in line with the online information dissemination process, and that the dissemination rate and recovery rate affect the early and late stages of information dissemination [27].…”

“…(3) An empirical approach [5,6,10,14,16], or quantitative research such as the LDA model [4,15,19], SIS model [11,24,25], and SIR model [12], are popularly used to investigate the emotional characteristics of COVID-19 public opinion dissemination in the existing literature. In the aforementioned studies, Wang and Cai [11], Suo and Guo [24], and Kandhway and Kuri [25] do not carry out an empirical analysis or case study. Moreover, the network analysis method used to study the COVID-19 public opinions is ignored.…”

“…Thus, the number of Weibo users and the initial number of negative information communicators both scaled down by 1/4 are set to 10,000 and 200, respectively. Meanwhile, according to Kandhway and Kuri [25], the spread rate β and the recovery rate γ is set to [0, 0.4], where β > γ. Since the threshold range of users' text emotional feature factors is [0, 1], the levels of positive emotion, negative emotion, and neutral emotion are set to [0, 1].…”

“…However, these suggestions ignore several factors (e.g., algorithmic technology, user literacy, and media ethics) that influence the current media environment. Third, the empirical method [5,6,10,14,16], Latent Dirichlet Allocation (LDA) model [4,15,19], SIS model [11,24,25], and Susceptible-Infective-Recovered (SIR) model [12] are popularly used to obtain guidance strategies for public opinion in the previous studies. The network analysis method is ignored.…”

Validation of the finite state machine model for network simulation and a proposed testbed

“…A goal of the CyAMS project is to be able to accurately model malware and malware propagation and, for this reason, epidemic modeling utilizing the susceptible, infected, recovered (SIR) and susceptible, infected, susceptible (SIS) models has become an area of interest. In the work, Accelerating information diffusion in social networks under the Susceptible-Infected-Susceptible epidemic model by Kandhway and Kuri, 4 the authors look at modifying these epidemic models to fit the spread of information via social networks. This type of epidemic model lends itself well to the creation of state machines, and for the CyAMS model this was expanded to the modeling of all applications, as well as systems.…”