Modeling and Change Detection for Count-Weighted Multilayer Networks

References

• Aicher, C., Jacobs, A. Z., and Clauset, A. (2015), “Learning Latent Block Structure in Weighted Networks,” Journal of Complex Networks, 3, 221–248. DOI: 10.1093/comnet/cnu026.
   Web of Science ®Google Scholar
• Airoldi, E. M., Blei, D. M., Fienberg, S. E., and Xing, E. P. (2008), “Mixed Membership Stochastic Blockmodels,” Journal of Machine Learning Research, 9, 1981–2014.
   PubMed Web of Science ®Google Scholar
• Azarnoush, B., Paynabar, K., and Bekki, J. (2016), “Monitoring Temporal Homogeneity in Attributed Network Streams,” Journal of Quality Technology, 48, 28. DOI: 10.1080/00224065.2016.11918149.
   Web of Science ®Google Scholar
• Biernacki, C., Celeux, G., and Govaert, G. (2000), “Assessing a Mixture Model for Clustering With the Integrated Completed Likelihood,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 719–725. DOI: 10.1109/34.865189.
   Web of Science ®Google Scholar
• Cheng, A., and Dickinson, P. (2013), “Using Scan-Statistical Correlations for Network Change Analysis,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin, Heidelberg: Springer, pp. 1–13.
   Google Scholar
• De Bacco, C., Power, E. A., Larremore, D. B., and Moore, C. (2017), “Community Detection, Link Prediction, and Layer Interdependence in Multilayer Networks,” Physical Review E, 95, 042317. DOI: 10.1103/PhysRevE.95.042317.
   PubMed Web of Science ®Google Scholar
• Erdős, P., and Rényi, A. (1960), “On the Evolution of Random Graphs,” Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–61.
   Google Scholar
• Gao, Z.-K., Dang, W.-D., Xue, L., and Zhang, S.-S. (2017), “Directed Weighted Network Structure Analysis of Complex Impedance Measurements for Characterizing Oil-in-Water Bubbly Flow,” Chaos, 27, 035805. DOI: 10.1063/1.4972562.
   PubMed Web of Science ®Google Scholar
• Girvan, M., and Newman, M. E. (2002), “Community Structure in Social and Biological Networks,” Proceedings of the National Academy of Sciences of the United States of America, 99, 7821–7826. DOI: 10.1073/pnas.122653799.
   PubMed Web of Science ®Google Scholar
• Goldenberg, A., Zheng, A. X., Fienberg, S. E., and Airoldi, E. M. (2010), “A Survey of Statistical Network Models,” Foundations and Trends® in Machine Learning, 2, 129–233. DOI: 10.1561/2200000005.
   Google Scholar
• Han, Q., Xu, K., and Airoldi, E. (2015), “Consistent Estimation of Dynamic and Multi-layer Block Models,” in International Conference on Machine Learning, pp. 1511–1520.
   Google Scholar
• Hoff, P. D., Raftery, A. E., and Handcock, M. S. (2002), “Latent Space Approaches to Social Network Analysis,” Journal of the American Statistical Association, 97, 1090–1098. DOI: 10.1198/016214502388618906.
   Web of Science ®Google Scholar
• Holland, P. W., Laskey, K. B., and Leinhardt, S. (1983), “Stochastic Blockmodels: First Steps,” Social Networks, 5, 109–137. DOI: 10.1016/0378-8733(83)90021-7.
   Web of Science ®Google Scholar
• Jun, C., and Shun-zheng, Y. (2009), “Network Monitoring Based on Community Structure Change,” in 2009 International Symposium on Intelligent Ubiquitous Computing and Education, IUCE, IEEE, pp. 337–340.
   Google Scholar
• Karrer, B., and Newman, M. E. J. (2011), “Stochastic Blockmodels and Community Structure in Networks,” Physical Review E, 83, 016107. DOI: 10.1103/PhysRevE.83.016107.
   PubMed Web of Science ®Google Scholar
• Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., and Porter, M. A. (2014), “Multilayer Networks,” Journal of Complex Networks, 2, 203–271. DOI: 10.1093/comnet/cnu016.
   Google Scholar
• Li, C.-S., Lu, J.-C., Park, J., Kim, K., Brinkley, P. A., and Peterson, J. P. (1999), “Multivariate Zero-Inflated Poisson Models and Their Applications,” Technometrics, 41, 29–38. DOI: 10.1080/00401706.1999.10485593.
   Web of Science ®Google Scholar
• Mariadassou, M., Robin, S., and Vacher, C. (2010), “Uncovering Latent Structure in Valued Graphs: A Variational Approach,” The Annals of Applied Statistics, 4, 715–742. DOI: 10.1214/10-AOAS361.
   Web of Science ®Google Scholar
• Miller, K. T., Griffiths, T. L., and Jordan, M. I. (2009), “Nonparametric Latent Feature Models for Link Prediction,” in International Conference on Neural Information Processing Systems, pp. 1276–1284.
   Google Scholar
• 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, 403–414. DOI: 10.1080/00401706.2013.822830.
   Web of Science ®Google Scholar
• Newman, M. E. J., and Park, J. (2003), “Why social Networks Are Different From Other Types of Networks,” Physical Review E, 68, 036122. DOI: 10.1103/PhysRevE.68.036122.
   Web of Science ®Google Scholar
• Nicosia, V., and Latora, V. (2015), “Measuring and Modeling Correlations in Multiplex Networks,” Physical Review E, 92, 032805. DOI: 10.1103/PhysRevE.92.032805.
   Web of Science ®Google Scholar
• Paul, S., and Chen, Y. (2016), “Consistent Community Detection in Multi-relational Data Through Restricted Multi-layer Stochastic Blockmodel,” Electronic Journal of Statistics, 10, 3807–3870. DOI: 10.1214/16-EJS1211.
   Web of Science ®Google Scholar
• Peel, L., and Clauset, A. (2015), “Detecting Change Points in the Large-Scale Structure of Evolving Networks,” in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI’15, AAAI Press, pp. 2914–2920.
   Google Scholar
• Priebe, C. E., Conroy, J. M., Marchette, D. J., and Park, Y. (2005), “Scan Statistics on Enron Graphs,” Computational & Mathematical Organization Theory, 11, 229–247. DOI: 10.1007/s10588-005-5378-z.
   Google Scholar
• Savage, D., Zhang, X., Yu, X., Chou, P., and Wang, Q. (2014), “Anomaly Detection in Online Social Networks,” Social Networks, 39, 62–70. DOI: 10.1016/j.socnet.2014.05.002.
   Web of Science ®Google Scholar
• Wang, Y. X. R., and Bickel, P. J. (2017), “Likelihood-Based Model Selection for Stochastic Block Models,” The Annals of Statistics, 45, 500–528. DOI: 10.1214/16-AOS1457.
   Web of Science ®Google Scholar
• Wilson, J. D., Stevens, N. T., and Woodall, W. H. (2016), “Modeling and Estimating Change in Temporal Networks via a Dynamic Degree Corrected Stochastic Block Model,” arXiv no. 1605.04049.
   Google Scholar
• 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, 354–365. DOI: 10.1080/0740817X.2016.1213468.
   Web of Science ®Google Scholar
• Xu, K. S., and Hero, A. O. (2013), “Dynamic Stochastic Blockmodels: Statistical Models for Time-Evolving Networks,” in International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, Berlin, Heidelberg: Springer, pp. 201–210.
   Google Scholar