127
Views
1
CrossRef citations to date
0
Altmetric
Articles

Monitoring the structure of social networks based on exponential random graph model

, ORCID Icon & ORCID Icon
Pages 3742-3757 | Received 02 Mar 2022, Accepted 23 Dec 2022, Published online: 03 Jan 2023

References

  • Azarnoush, B., K. Paynabar, J. Bekki, and G. Runger. 2016. Monitoring temporal homogeneity in attributed network streams. Journal of Quality Technology 48 (1):28–43. doi:10.1080/00224065.2016.11918149.
  • Frank, O., and D. Strauss. 1986. Markov graphs. Journal of the American Statistical Association 81 (395):832–42. doi:10.1080/01621459.1986.10478342.
  • Handcock, M. S., D. R. Hunter, C. T. Butts, S. M. Goodreau, P. N. Krivitsky, M. Morris, and M. P. N. Krivitsky. 2019. Package ‘ergm. userterms’. https://cran.ism.ac.jp/web/packages/ergm.userterms/ergm.userterms.
  • Heard, N. A., D. J. Weston, K. Platanioti, and D. J. Hand. 2010. Bayesian anomaly detection methods for social networks. The Annals of Applied Statistics 4 (2):645–62.
  • Jensen, W. A., and J. B. Birch. 2009. Profile monitoring via nonlinear mixed model. Journal of Quality Technology 41 (1):18–34. doi:10.1080/00224065.2009.11917757.
  • Kazemzadeh, R. B., R. Noorossana, and A. Amiri. 2008. Phase I monitoring of polynomial profiles. Communications in Statistics - Theory and Methods 37 (10):1671–86. doi:10.1080/03610920701691714.
  • Priebe, C. E., J. M. Conroy, D. J. Marchette, and Y. Park. 2005. Scan statistics on enron graphs. Computational and Mathematical Organization Theory 11 (3):229–47. doi:10.1007/s10588-005-5378-z.
  • Rajabi, F., A. Saghaei, and S. Sadinejad. 2020. Monitoring of social network and change detection by applying statistical process: ERGM. Journal of Optimization in Industrial Engineering 13 (1):131–43.
  • Ranshous, S., S. Shen, D. Koutra, S. Harenberg, C. Faloutsos, and N. F. Samatova. 2015. Anomaly detection in dynamic networks: A survey. Wiley Interdisciplinary Reviews: Computational Statistics 7 (3):223–47. doi:10.1002/wics.1347.
  • Robins, G., P. Pattison, Y. Kalish, and D. Lusher. 2007. An introduction to exponential random graph (p*) models for social networks. Social Networks 29 (2):173–91. doi:10.1016/j.socnet.2006.08.002.
  • Strauss, D., and M. Ikeda. 1990. Pseudolikelihood estimation for social networks. Journal of the American Statistical Association 85 (409):204–12. doi:10.1080/01621459.1990.10475327.
  • Van Duijn, M. A., K. Gile, and M. S. Handcock. 2007. Comparison of maximum pseudo likelihood and maximum likelihood estimation of exponential family random graph models. Working Paper 74. Center for Statistics and the Social Sciences, University of Washington, Washington. http://www.Csss.Washington.Edu/Papers.
  • Wang, K., & Tsung, F. (2005). Using profile monitoring techniques for a data-rich environment with huge sample size. Quality and Reliability Engineering International, 21(7), 677–88.
  • Wasserman, S., & Robins, G. (2005). An introduction to random graphs, dependence graphs, and p*. Models and Methods in Social Network Analysis, 27, 148–61. doi:10.1002/qre.711.
  • Woodall, W. H. 2007. Current research on profile monitoring. Production 17 (3):420–5. doi:10.1590/S0103-65132007000300002.
  • Woodall, W. H., and D. C. Montgomery. 1999. Research issues and ideas in statistical process control. Journal of Quality Technology 31 (4):376–86. doi:10.1080/00224065.1999.11979944.
  • Woodall, W. H., M. J. Zhao, K. Paynabar, R. Sparks, and J. D. Wilson. 2017. An overview and perspective on social network monitoring. IISE Transactions 49 (3):354–65. doi:10.1080/0740817X.2016.1213468.
  • Zhou, P., D. K. Lin, X. Niu, and Z. He. 2020. Performance evaluation method for network monitoring based on separable temporal exponential random graph models with application to the study of autocorrelation effects. Computers & Industrial Engineering 145 (1):106507. doi:10.1016/j.cie.2020.106507.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.