چكيده به لاتين
Abstract:
Communication phenomena occur in many areas and are increasingly seen by networks. For this reason, networks need to have realistic and reliable statistical models, especially when phenomena progress over time. For this purpose, the models used must have a real time structure and cross-sectional structure. After conducting studies in the field of social networks monitoring, the hypothesis is presented as a model for controlling the changes in the STERGM networks. Many real-world networks follow this model, so this is a requirement that this model Networks are monitored. In this study, firstly, a first-order Markov chain of ERGM networks was simulated in discrete time periods using MATLAB software; and the intermediate chain consisting of shared and interconnected networks and network density in three modes (Markov chain, formation and dissolution), and the best mode for monitoring the density of the network is the community mode, and the optimal state-of-the-art optimal control chart is the best performance using the average length of the sequence (ARL), the cumulative accumulation control chart (CUSUM).
In another section of the research, the probability of the networks between the networks is estimated in all three cases and finally, the rate of communication between the nodes in the network is calculated and with Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) control charts In order to control the amount of variation, the best control curve (EWMA) and for large groups (20 and 35) and Cumulative Control Cumulative Chart (CUSUM) are the most desirable charts for small groups (5 and 10). This model is monitored by the network.
Keywords: Social networks monitoring, separable temporal exponential random graph model (STERGM), Exponentially weighted moving average (EWMA) control chart, Cumulative sum (CUSUM) control chart, Average run length (ARL)
