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Individual behaviors, such as drinking, smoking, screen time, and physical activity, can be strongly influenced by the behavior of friends. At the same time, the choice of friends can be influenced by shared behavioral preferences. The actor-based stochastic models ABSM are developed to study the interdependence of social networks and behavior. These methods are efficient and useful for analysis of discrete behaviors, such as drinking and smoking; however, since the behavior evolution function is in an exponential format, the ABSM can generate inconsistent and unrealistic results when the behavior variable is continuous or has a large range, such as hours of television watched or body mass index. To more realistically model continuous behavior variables, we propose a co-evolution process based on a linear model which is consistent over time and has an intuitive interpretation. In the simulation study, we applied the expectation maximization EM and Markov chain Monte Carlo MCMC algorithms to find the maximum likelihood estimate MLE of parameter values. Additionally, we show that our assumptions are reasonable using data from the National Longitudinal Study of Adolescent Health Add Health.


Social Network, Social Behavior, Co-Evolution, Markov Chain, Stationary Distribution

Cite this paper

Tong, L. , Shoham, D. and Cooper, R. 2014 A Co-Evolution Model for Dynamic Social Network and Behavior. Open Journal of Statistics, 4, 765-775. doi: 10.4236-ojs.2014.49072.

Author: Liping Tong, David Shoham, Richard S. Cooper

Source: http://www.scirp.org/


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