Loading...

Monitoring autoregressive binary social networks based on likelihood statistics

Taheri, Z ; Sharif University of Technology | 2020

445 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.cie.2020.106721
  3. Publisher: Elsevier Ltd , 2020
  4. Abstract:
  5. Network monitoring is a new area in statistical process control applications. It aims at detecting assignable changes in the communication structure of a network. The probability of communications in social networks is usually based on the attributes of vertices. Moreover, due to the nature of human relationships, social networks are almost time-dependent. Neglecting this feature in control chart design reduces the chart performance. In this paper, communications are defined as autoregressive binary variables with the probability modeled by the logit link function. The explanatory variables of the model are the vertices’ attributes and previous information of the network. Accordingly, we propose three likelihood ratio test-based methods, one static and two dynamic reference methods. The performance of the proposed methods is evaluated using simulation studies and real numerical examples from the email communications of Enron Corporation. Then, the effect of the autocorrelation structure on the link function is investigated. Also, the effect of parameter estimation on the ARL measure of the proposed methods is studied. Furthermore, the performance of the proposed methods is compared with three traditional methods. Finally, some practical suggestions are given for different out-of-control situations and statistical designs. © 2020
  6. Keywords:
  7. Autoregressive model ; Binary response ; Likelihood estimation ; Logit link function ; Social network ; Numerical methods ; Autocorrelation structures ; Communication structures ; Email communication ; Explanatory variables ; Human relationships ; Likelihood ratio tests ; Simulation studies ; Statistical design ; Statistical process control
  8. Source: Computers and Industrial Engineering ; Volume 149 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0360835220304459