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Data Science, Quality & Reliability

Log-linear stochastic block modeling and monitoring of directed sparse weighted network systems

, &
Pages 515-526 | Received 10 Oct 2021, Accepted 01 Apr 2023, Published online: 30 May 2023

References

  • Abbe, E. (2017) Community detection and stochastic block models: Recent developments. The Journal of Machine Learning Research, 18(1), 6446–6531.
  • Airoldi, E.M., Blei, D., Fienberg, S. and Xing, E. (2008) Mixed membership stochastic blockmodels. The Journal of Machine Learning Research, 9(65), 1981–2014.
  • Alghuried, A. and Moghaddass, R. (2021) Anomaly detection in large-scale networks: A state-space decision process. Journal of Quality Technology, 54(1), 65–92.
  • Anderson, T.W. (2003) An Introduction to Multivariate Statistical Analysis. John Wiley & Sons, New York, NY.
  • Cameron, A.C. and Trivedi, P.K. (2013) Regression Analysis of Count Data. Cambridge University Press, New York, NY.
  • Dong, H., Chen, N. and Wang, K. (2020) Modeling and change detection for count-weighted multilayer networks. Technometrics, 62,184–195.
  • Dong, H. and Wang, K. (2022) Interaction event network modeling based on temporal point process. IISE Transactions, 54(7), 630–642.
  • Ebrahimi, S., Reisi-Gahrooei, M., Paynabar, K. and Mankad, S. (2021) Monitoring sparse and attributed networks with online hurdle models. IISE Transactions, 54(1), 91–104.
  • Elhabashy, A.E., Wells, L.J. and Camelio, J.A. (2019) Cyber-physical security research efforts in manufacturing–a literature review. Procedia Manufacturing, 34, 921–931.
  • Gahrooei, M.R. and Paynabar, K. (2018) Change detection in a dynamic stream of attributed networks. Journal of Quality Technology, 50(4), 418–430.
  • Girvan, M. and Newman, M.E. (2002) Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.
  • Hero, A. and Rajaratnam, B. (2011) Large-scale correlation screening. Journal of the American Statistical Association, 106(496), 1540–1552.
  • Holland, P.W., Laskey, K.B. and Leinhardt, S. (1983) Stochastic blockmodels: First steps. Social Networks, 5(2), 109–137.
  • Jiao, P., Li, T., Wu, H., Wang, C.-D., He, D. and Wang, W. (2022) HB-DSBM: Modeling the dynamic complex networks from community level to node level. IEEE Transactions on Neural Networks and Learning Systems, in press.
  • Jones, L.A., Champ, C.W. and Rigdon, S.E. (2001) The performance of exponentially weighted moving average charts with estimated parameters. Technometrics, 43(2), 156–167.
  • Karrer, B. and Newman, M.E. (2011) Stochastic blockmodels and community structure in networks. Physical Review E, 83(1), 016107.
  • Lee, C. and Wilkinson, D.J. (2019) A review of stochastic block models and extensions for graph clustering. Applied Network Science, 4(1), 1–50.
  • Lei, Y., Yuan, Y. and Zhao, J. (2013) Model-based detection and monitoring of the intermittent connections for can networks. IEEE Transactions on Industrial Electronics, 61(6), 2912–2921.
  • Mo, H., Sansavini, G. and Xie, M. (2021) Cyber-Physical Distributed Systems: Modeling, Reliability Analysis and Applications. John Wiley & Sons, New York, NY.
  • Montgomery, D.C. (2020) Introduction to Statistical Quality Control. John Wiley & Sons, New York, NY.
  • Motalebi, N., Owlia, M.S., Amiri, A. and Fallahnezhad, M.S. (2023) Monitoring social networks based on zero-inflated Poisson regression model. Communications in Statistics-Theory and Methods, 52(7), 2099–2115.
  • Motalebi, N., Stevens, N.T. and Steiner, S.H. (2021) Hurdle blockmodels for sparse network modeling. The American Statistician, 75(4), 383–393.
  • Neil, J., Hash, C., Brugh, A., Fisk, M. and Storlie, C.B. (2013) Scan statistics for the online detection of locally anomalous subgraphs. Technometrics, 55(4), 403–414.
  • Perry, M.B. (2020) An EWMA control chart for categorical processes with applications to social network monitoring. Journal of Quality Technology, 52(2),182–197.
  • Pons, P. and Latapy, M. (2005) Computing communities in large networks using random walks, in International Symposium on Computer and Information Sciences, Springer, Berlin, Heidelberg, pp. 284–293.
  • Priebe, C., Conroy, J.M., Marchette, D. and Park, Y. (2005) Scan statistics on Enron graphs. Computational & Mathematical Organization Theory, 11(2), 229–247.
  • Qiu, P. (2013) Introduction to Statistical Process Control. CRC Press, Boca Raton, FL.
  • Sparks, R. (2016) Detecting periods of significant increased communication levels for subgroups of targeted individuals. Quality and Reliability Engineering International, 32(5), 1871–1888.
  • Sparks, R. and Wilson, J.D. (2019), Monitoring communication outbreaks among an unknown team of actors in dynamic networks. Journal of Quality Technology, 51(4), 353–374.
  • Wang, D., Li, F. and Liu, K. (2023) Modeling and monitoring of a multivariate spatio-temporal network system. IISE Transactions, 55(4), 331–347.
  • Wang, K. and Li, J. (2018) Integration of sparse singular vector decomposition and statistical process control for traffic monitoring and quality of service improvement in mission-critical communication networks. IISE Transactions, 50(12), 1104–1116.
  • Wilson, J.D., Stevens, N.T. and Woodall, W.H. (2019) Modeling and detecting change in temporal networks via the degree corrected stochastic block model. Quality and Reliability Engineering International, 35(5), 1363–1378.
  • Woodall, W.H., Zhao, M.J., Paynabar, K., Sparks, R. and Wilson, J.D. (2017) An overview and perspective on social network monitoring. IISE Transactions, 49(3), 354–365.
  • Yang, H., Kumara, S., Bukkapatnam, S.T. and Tsung, F. (2019) The internet of things for smart manufacturing: A review. IISE Transactions, 51(11), 1190–1216.
  • Yang, W., Chen, J., Zhang, C. and Paynabar, K. (2022) Online detection of cyber-incidents in additive manufacturing systems via analyzing multimedia signals. Quality and Reliability Engineering International, 38(3), 1340–1356.
  • Yu, L., Woodall, W. and Tsui, K. (2018) Detecting node propensity changes in the dynamic degree corrected stochastic block model. Social Networks, 54, 209–227.
  • Yu, L., Zwetsloot, I.M., Stevens, N.T., Wilson, J.D. and Tsui, K.L. (2022) Monitoring dynamic networks: A simulation-based strategy for comparing monitoring methods and a comparative study. Quality and Reliability Engineering International, 38(3), 1226–1250.
  • Zhao, M.J., Driscoll, A.R., Sengupta, S., Fricker Jr, R.D., Spitzner, D.J. and Woodall, W.H. (2018) Performance evaluation of social network anomaly detection using a moving window–based scan method. Quality and Reliability Engineering International, 34(8), 1699–1716.
  • Zhou, Z. and Amini, A.A. (2019) Analysis of spectral clustering algorithms for community detection: The general bipartite setting. The Journal of Machine Learning Research, 20(1), 1774–1820.
  • Zou, C. and Qiu, P. (2009) Multivariate statistical process control using LASSO. Journal of the American Statistical Association, 104(488), 1586–1596.

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