A Model for Competing Information Diffusion in Social Networks

Abstract

In recent years, social networks have attracted the interest of researchers from diverse disciplines. Competing types of information, such as positive and negative information about a topic or marketing information for similar products from different companies, often diffuse in social networks simultaneously. However, most previous studies only consider one type of information using the susceptible-infectiousrecovered or susceptible-infectious-susceptible models. In this paper, we propose a competitive diffusion model to describe the diffusion processes of two types of information in social networks, analyze the stability of the diffusion infection-free equilibrium, and study the diffusion processes by numerical simulations. The results show that the information with the larger probabilities of being received and reposted diffuses more broadly and suppresses the competitor. We also find that the initial number of spreader nodes, the degrees of participants, and network structures all affect the scope of information diffusion.

A novel information diffusion model based on psychosocial factors with automatic parameter learning

“…In [13], an extension of the Susceptible-Infected diffusion model is proposed, in which the authors include elements of human dynamics, such as bursty and limited attention, with a significant impact on the diffusion process. In [14] a competitive model of information diffusion is presented, which consists in the simultaneous spread of two different pieces of information. A diffusion model called GT is presented in [15], in which the nodes are considered intelligent and rational agents and have two types of payoff: a social and an individual one.…”

“…The elapsed time (14) is the difference between the current simulation time (Simulation_clock) and the start time T. We normalize T elapsed according to a maximum socialization time (T max_socialization ) to obtain a period expressed in percentages and which is specific to each node. Thus, Attenuation Ps tends to 0 (maximum attenuation for P s ) as T elapsed tends to 100%.…”

Real-Time Opinion Prediction Method for Emergency Public Events in Social Media Networks Using Opinion Hit Matrix

“…Manoharan and Senthilkumar [32] developed a personalized news recommendation system based on intelligent fuzzy-rule, which recommends personalized news articles based on user profile and interest categories in social media like Facebook and Twitter. Sun et al [33] proposed a competitive diffusion model to analyze and study the competing information diffusion in social networks. This model analyzes the competing information like positive, neutral, and negative information about a product, topic, or any events to affect the scope of the diffusion information.…”

Exploring the Dynamical Behavior of Information Diffusion in D2D Communication Environment

“…Lin et al [27] proposed a susceptible-wanderinginfected-recovery (SWIR) model to describe the influence of individual psychological state change on the dissemination of fraud information and studied the control problem of fraud information with personal loss as a constraint. Sun et al [28] proposed an improved SIR model to analyze the competitive diffusion of positive and negative information and analyzed the stability of diffusion infection-free equilibrium. Yi et al [29] introduced the uncertain individual called hesitator in the SIR model and utilized the selfconfirmation mechanism to describe the influence of social tie strength on user-forwarding behavior.…”