Rajabi, F., Saghaei, A., Sadinejad, S. (2019). Monitoring of Social Network and Change Detection by Applying Statistical Process: ERGM. Journal of Optimization in Industrial Engineering, (), -. doi: 10.22094/joie.2019.581174.1615
Frshid Rajabi; Abbas Saghaei; Soheil Sadinejad. "Monitoring of Social Network and Change Detection by Applying Statistical Process: ERGM". Journal of Optimization in Industrial Engineering, , , 2019, -. doi: 10.22094/joie.2019.581174.1615
Rajabi, F., Saghaei, A., Sadinejad, S. (2019). 'Monitoring of Social Network and Change Detection by Applying Statistical Process: ERGM', Journal of Optimization in Industrial Engineering, (), pp. -. doi: 10.22094/joie.2019.581174.1615
Rajabi, F., Saghaei, A., Sadinejad, S. Monitoring of Social Network and Change Detection by Applying Statistical Process: ERGM. Journal of Optimization in Industrial Engineering, 2019; (): -. doi: 10.22094/joie.2019.581174.1615
Monitoring of Social Network and Change Detection by Applying Statistical Process: ERGM
Articles in Press, Accepted Manuscript , Available Online from 22 September 2019
Industrial Engineering Department, School of Engineering, Science and Research Branch, Islamic Azad University (Hesarak Ave. Ashrafi Esfehani blvd.), Tehran, Iran.
Abstract
The statistical modeling of social network data needs much effort because of the complex dependence structure of the tie variables. In order to formulate such dependences, the statistical exponential families of distributions can provide a flexible structure. In this regard, the statistical characteristics of the network is provided to be encapsulated within an Exponential Random Graph Model (ERGM). Applying the ERGM, in this paper, we follow to design a statistical process control through network behavior. The results demonstrated the superiority of the designed chart over the existing change detection methods in controlling the states. Additionally, the detection process is formulated for the social networks and the results are statistically analyzed.
Graphical Abstract
Highlights
In general, the current research was aimed at providing a statistical method based on statistical knowledge of the network, which is also significant and valuable. Initially, the network was depicted in the form of a mathematical model, and by applying the quality engineering issues and defining the appropriate statistics, as well as developing an acceptable framework and methodology, to design the boundaries and control modules. Finally, the accuracy of the proposed method was examined and verified.
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