Volume 29, Issue 3 (IJIEPR 2018)                   IJIEPR 2018, 29(3): 247-259 | Back to browse issues page

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Mazrae Farahani E, Baradaran Kazemzade R, Albadvi A, Teimourpour B. Modeling and Monitoring Social Ntwork in term of Longitudinal Data. IJIEPR. 2018; 29 (3) :247-259
URL: http://ijiepr.iust.ac.ir/article-1-808-en.html
1- , rkazem@gmail.com
Abstract:   (1230 Views)
Studying the social networks plays a significant role in everyone’s life. Recent studies show the importance and increasing interests in the subject by modeling and monitoring the communications between the network members as longitudinal data. Typically, the tendency for modeling the social networks with considering the dependency of an outcome variable on the covariates is growing recently. However, these studies fail in considering the possible correlation between the responses in the modeling of social networks. Our study use generalized linear mixed models (GLMMs) (also referred to as random effects models) to model the social network according to the attributes of nodes in which the nodes take a role of random effect or hidden effect in the modeling. The likelihood ratio test (LRT) statistics is implemented to detect change points in the simulated network streams. Also, in the simulation studies, we applied root mean square Error (RMSE) and standard deviation criteria for choosing an appropriate distribution for simulation data. Also, our simulation studies demonstrates an improvement in the average run length (ARL) index in comparison to the previous studies.
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Type of Study: Research | Subject: Statistical Process Control Statistical Process Control or Quality Control
Received: 2018/01/26 | Accepted: 2018/08/5 | Published: 2018/09/23

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