id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-155440-7l8tatwq	Malinovskaya, Anna	Online network monitoring	2020-10-19		.txt	text/plain	5710	326	54	Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks determined by temporal exponential random graph models (TERGM). The leading SPC tool for analysis is a control chart, which exists in various forms in terms of the number of variables, data type and different statistics being of interest. To conduct surveillance over Y t , we propose to consider only the dynamically estimated parameters of a random graph model in order to reduce computational complexity and to allow for real-time monitoring. In this case, as well as fine-tuning the configuration of statistics, one can modify some settings which design the estimation procedure of the model parameter, for example, the run time, the sample size or the step length (Morris et al., 2008) . In this paper, we show how multivariate control charts can be used to detect changes in TERGM networks. Monitoring of social network and change detection by applying statistical process: ERGM	./cache/cord-155440-7l8tatwq.txt	./txt/cord-155440-7l8tatwq.txt