References
- Chen H, Zhang N. Graph-based change-point detection. Ann Stat. 2015;43(1):139–176.
- Chen H, Sequential change-point detection based on nearest neighbors. Preprint 2018. Available at arXiv:1604.03611.
- Roy RB, Sarkar UK. A social network approach to change detection in the interdependence structure of global stock markets. Soc Netw Anal Min. 2013;3:269–283.
- McCulloh I, Carley KM, Detecting change in human social behavior simulation. Technical Report CMU-ISR-08-135, Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh (PA); 2008a.
- McCulloh I, Carley KM, Social network change detection. Technical Report CMU-ISR-08-116, Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh (PA); 2008b.
- McCulloh I, Carley KM. Detecting change in longitudinal social networks. J Soc Struct. 2011;12(1):1–37.
- Heard NA, Weston DJ, Platanioti K, et al. Bayesian anomaly detection methods for social networks. Ann Appl Stat. 2010;4(2):645–662.
- Sparks R. Social network monitoring: aiming to identify periods of unusually increased communications between parties of interest. In: Knoth S, Schmid W, editors. Frontiers in statistical quality control Vol. 11, Heidelberg: Springer-Verlag; 2015. p. 3–13.
- Frank O, Strauss D, Markov graphs. J Am Stat Assoc. 1986;81:832–842.
- Wasserman S, Pattison PE. Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*. Psychometrika. 1996;61:401–425.
- Vu DQ, Hunter DR, Schweinberger M. Model-based clustering of large networks. Ann Appl Stat. 2013;7(2):1010–1039.
- Hanneke S, Fu W, Xing E. Discrete temporal models of social networks. Electron J Stat. 2010;4:585–605.
- Krivitsky PN, Handcock MS. A separable model for dynamic networks. J R Stat Soc Ser B. 2014;76(1):29–46.
- Montgomery DC. Introduction to statistical quality control. 3rd ed., New York: Wiley; 1996.
- Lai TL. Sequential change-point detection in quality control and dynamic systems. J Roy Statist Soc Ser B. 1995;57:613–658.
- Qiu P. Introduction to statistical process control. Boca Raton (FL): Chapman & Hall/CRC; 2014.
- Siegmund D, Venkatraman ES. Using the generalized likelihood ratio statistic for sequential detection of a change-point. Ann Stat. 1995;23:255–271.
- Han D, Tsung F. A generalized EWMA control chart and its comparison with the optimal EWMA, CUSUM and GLR schemes. Ann Stat. 2004;32:316–339.
- Hawkins DM, Deng Q. A nonparametric change point control chart. J Qual Technol. 2010;42(2):165–173.
- Lau TS, Tay WP, Veeravalli VV, Quickest change detection with unknown post-change distribution. IEEE International Conference on Acoustics; IEEE. 2017.
- Lorden G. Procedures for reacting to a change in distribution. Ann Math Stat. 1971;42:1897–1908.
- Lucas JM. Combined Shewhart-CUSUM quality control scheme. J Qual Tech. 1982;14:51–59.
- Sparks RS. CUSUM charts for signalling varying location shifts. J Qual Tech. 2000;32:157–171.
- Han D, Tsung F, Hu X, et al. CUSUM and EWMA multi-charts for detection a range of mean shifts. Stat Sin. 2007;17:1139–1164.
- Han D, Tsung F. Detection and diagnosis of unknown abrupt changes using CUSUM multi-chart schemes. Sequan Anal. 2007;26(3):225–249.
- Capizzi G, Masarotto G. Efficient control chart calibration by simulated stochastic approximation. IIE Trans. 2016;48(1):57–65.
- Geng J, Bayraktar E, Lai L. Multi-chart detection procedure for Bayesian quickest change-point detection with unknown post-change parameters. Preprint 2017. Available at arXiv:1708.06901.
- Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika. 1994;3(3):425–455.
- Moustakides GV. Optimal stopping times for detecting changes in distributions. Ann Stat. 1986;14(4):1379–1387.
- Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001;96:1348–1360.
- Siegmund D. Sequential analysis: tests and confidence intervals. New York: Springer-Verlag; 1985.
- Karrer B, Newman MEJ. Stochastic blockmodels and community structure in networks. Phys Rev E. 2011;83:016107.
- Erdos P, Rnyi A. On random graphs I. Pub Math. 1959;6:290–297.
- Ryu JH, Wan HG, Kim S. Optimal design of a CUSUM chart for a mean shift of unknown size. J Qual Technol. 2010;42(3):19–20.
- Gut A. Stopping time random walks: limit theorems and applications. New York: Springer; 1988.
- Barabsi AL, Bonabeau E. Scale-free networks. Sci Am. 2003;288(5):60–9.
- Castagliola P, Maravelakis P E. A CUSUM control chart for monitoring the variance when parameters are estimated. J Stat Plan Inference. 2011;141(4):1463–1478.
- Hunter DR, Handcock MS, Butts CT. ergm: A package to fit, simulate and diagnose exponential-family models for networks. J Stat Softw. 2008;24(3):1–29.
- Erdos P, Rnyi A. On the evolution of random graphs. Pub Math Inst Hungarian Acad Sci. 1960;5:17–61.
- Harris JK. An introduction to exponential random graph modeling. California: Sage Publications; 2013.
- Krapivsky PL, Rodgers GJ, Redner S. Degree distributions of growing networks. Phys Rev Lett. 2001;86(23):5401–4.
- Koller D, Friedman N. Probabilistic graphical models: principles and techniques. Cambridge: MIT Press; 2009.
- Qiao L, Han D, Optimal sequential tests for detection of changes under finite measure space for finite sequences of networks. Preprint 2019. Available at arXiv:1911.06545