Simulating the Diffusion of Information: An Agent-Based Modeling Approach

Simulating the Diffusion of Information: An Agent-Based Modeling Approach

Cindy Hui (Rensselaer Polytechnic Institute, USA), Mark Goldberg (Rensselaer Polytechnic Institute, USA), Malik Magdon-Ismail (Rensselaer Polytechnic Institute, USA) and William A. Wallace (Rensselaer Polytechnic Institute, USA)
Copyright: © 2010 |Pages: 16
DOI: 10.4018/jats.2010070103
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Diffusion occurs in various contexts and generally involves a network of entities and interactions between entities. Through these interactions, some property, e.g., information, ideas, etc., is spread through the network. This paper presents a general model of diffusion in dynamic networks. The authors simulate the diffusion of evacuation warnings in multiple network structures under various model settings and observe the proportion of evacuated nodes. The network dynamics occur as the result of the diffusion where nodes may leave the network after receiving the warning. The authors use the model to explore how the network structure, seeding strategy, network trust, and trust distribution affect the diffusion process. The effectiveness of the diffusion is a function of the network structure and seeding strategy used in delivering the initial broadcast. The simulation results reveal interesting observations on the effects of network trust and distribution of trust in the network.
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Social networks play a significant role in the spread of information, ideas, emotions, diseases, innovations, etc. As a result, the flows of information, ideas, etc. affect the way people think, act, and bind together in a society. Modeling information flow through various social networks is an active research area, with work on diffusion of innovation and technology (Bass, 2004; Brown & Reignen, 1987; Hill, Provost, & Volinsky, 2006; Rogers, 1995; Valente, 1995; Young, 2000), viral marketing (Leskovec, Adamic, & Huberman, 2006; Leskovec, Singh, & Kleinberg, 2006), the spread of computer viruses (R Albert, Jeong, & Barabasi, 2000; Chen & Carley, 2004), and the spread of diseases (Meyers, Newman, & Pourbohloul, 2006; Morris, 2000).

The spread of infectious diseases and the spread of infectious ideas have common characteristics in terms of their diffusion process. For this reason, many diffusion models for studying the spread of ideas were developed based on models from epidemiology (Bettencourt, Cintron-Arias, Kaiser, & Chavez, 2006). Many of the epidemiology models are derived from the Susceptible/Infected/Removed (SIR) model, which was formulated by Lowell Reed and Wade Hampton Frost in the 1920s (M. E. Newman, 2002). The SIR model divides the population into three possible categories (susceptible, infected, and removed) that reflect the status of the individuals. Susceptible are individuals who are not infected but may become infected when they gain contact with an infected individual. Infected are individuals who are carrying the disease and have the potential to spread it. Removed are individuals who have either recovered from the disease or died, and cannot spread the disease. The model assigns a disease transmission probability based on a given average rate of contact, and assumes that all individuals are equally likely to become infected. Mathematical models can then be used to infer population average parameters such as contact rates and duration of infectious periods.

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