Joint Network Reconstruction and Community Detection from Rich but Noisy Data

References

• Airoldi, E. M., Blei, D. M., Fienberg, S. E., and Xing, E. P. (2008), “Mixed Membership Stochastic Blockmodels,” The Journal of Machine Learning Research, 9, 1981–2014.
   PubMed Web of Science ®Google Scholar
• Amini, A. A., Chen, A., Bickel, P. J., and Levina, E. (2013), “Pseudo-Likelihood Methods for Community Detection in Large Sparse Networks,” The Annals of Statistics, 41, 2097–2122. DOI: 10.1214/13-AOS1138.
   Web of Science ®Google Scholar
• Bosma, J. J., Koetter, M., and Wedow, M. (2019), “Too Connected to Fail? Inferring Network Ties from Price Co-movements,” Journal of Business & Economic Statistics, 37, 67–80. DOI: 10.1080/07350015.2016.1272459.
   Web of Science ®Google Scholar
• Brownlees, C., Guðmundsson, G. S., and Lugosi, G. (2022), “Community Detection in Partial Correlation Network Models,” Journal of Business & Economic Statistics, 40, 216–226. DOI: 10.1080/07350015.2020.1798241.
   Web of Science ®Google Scholar
• Chen, S., and Schienle, M. (2022), “Large Spillover Networks of Nonstationary Systems,” Journal of Business & Economic Statistics. DOI: 10.1080/07350015.2022.2099870.
   PubMed Web of Science ®Google Scholar
• Chen, Y., Hu, J., and Zhang, W. (2020), “Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network,” China & World Economy, 28, 78–100. DOI: 10.1111/cwe.12357.
   Web of Science ®Google Scholar
• Eagle, N., and Pentland, A. S. (2006), “Reality Mining: Sensing Complex Social Systems,” Personal and Ubiquitous Computing, 10, 255–268. DOI: 10.1007/s00779-005-0046-3.
   Web of Science ®Google Scholar
• Gao, C., Ma, Z., Zhang, A. Y., and Zhou, H. H. (2017), “Achieving Optimal Misclassification Proportion in Stochastic Block Models,” The Journal of Machine Learning Research, 18, 1980–2024.
   Web of Science ®Google Scholar
• Gao, X., and Song, P. X.-K. (2010), “Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data,” Journal of the American Statistical Association, 105, 1531–1540. DOI: 10.1198/jasa.2010.tm09414.
   Web of Science ®Google Scholar
• Guimerà, R., and Sales-Pardo, M. (2009), “Missing and Spurious Interactions and the Reconstruction of Complex Networks,” Proceedings of the National Academy of Sciences, 106, 22073–22078. DOI: 10.1073/pnas.0908366106.
   PubMed Web of Science ®Google Scholar
• Han, X., Shen, Z., Wang, W.-X., and Di,Z. (2015), “Robust Reconstruction of Complex Networks from Sparse Data,” Physical Review Letters, 114, 028701. DOI: 10.1103/PhysRevLett.114.028701.
   PubMed Web of Science ®Google Scholar
• Hu, J., Zhang, J., Qin, H., Yan, T., and Zhu, J. (2021), “Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block Models,” Journal of the American Statistical Association, 116, 1373–1382. DOI: 10.1080/01621459.2020.1722676.
   Web of Science ®Google Scholar
• Jiang, B., Li, J., and Yao, Q. (2020), “Autoregressive Networks,” arXiv preprint arXiv:2010.04492.
   Google Scholar
• Keribin, C. (2000), “Consistent Estimation of the Order of Mixture Models,” Sankhyā: The Indian Journal of Statistics, Series A, 62, 49–66.
   Google Scholar
• Le, C. M., Levin, K., and Levina, E. (2018), “Estimating a Network From Multiple Noisy Realizations,” Electronic Journal of Statistics, 12, 4697–4740. DOI: 10.1214/18-EJS1521.
   Web of Science ®Google Scholar
• Lei, J., and Lin, K. (2020), “Discussion of ‘Network Cross-Validation by Edge Sampling,” Biometrika, 107, 285–287. DOI: 10.1093/biomet/asaa009.
   Google Scholar
• Lei, J., and Rinaldo, A. (2015), “Consistency of Spectral Clustering in Stochastic Block Models,” The Annals of Statistics, 43, 215–237. DOI: 10.1214/14-AOS1274.
   Web of Science ®Google Scholar
• Matias, C., and Miele, V. (2017), “Statistical Clustering of Temporal Networks through a Dynamic Stochastic Block Model,” Journal of the Royal Statistical Society, Series B, 79, 1119–1141. DOI: 10.1111/rssb.12200.
   Google Scholar
• Meng, X.-L., and Rubin, D. B. (1993), “Maximum Likelihood Estimation via the ECM Algorithm: A General Framework,” Biometrika, 80, 267–278. DOI: 10.1093/biomet/80.2.267.
   Web of Science ®Google Scholar
• Newman, M. E. (2018), “Network Structure From Rich but Noisy Data,” Nature Physics, 14, 542–545. DOI: 10.1038/s41567-018-0076-1.
   Web of Science ®Google Scholar
• Newman, M. E., and Leicht, E. A. (2007), “Mixture Models and Exploratory Analysis in Networks,” Proceedings of the National Academy of Sciences, 104, 9564–9569. DOI: 10.1073/pnas.0610537104.
   PubMed Web of Science ®Google Scholar
• Peixoto, T. P. (2019), “Network Reconstruction and Community Detection from Dynamics,” Physical Review Letters, 123, 128301. DOI: 10.1103/PhysRevLett.123.128301.
   PubMed Web of Science ®Google Scholar
• Runge, J., Heitzig, J., Petoukhov, V., and Kurths, J. (2012), “Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy,” Physical Review Letters, 108, 258701. DOI: 10.1103/PhysRevLett.108.258701.
   PubMed Web of Science ®Google Scholar
• Saldana, D. F., Yu, Y., and Feng, Y. (2017), “How Many Communities Are There?” Journal of Computational and Graphical Statistics, 26, 171–181. DOI: 10.1080/10618600.2015.1096790.
   Web of Science ®Google Scholar
• Shen, Z., Wang, W.-X., Fan, Y., Di, Z., and Lai, Y.-C. (2014), “Reconstructing Propagation Networks with Natural Diversity and Identifying Hidden Sources,” Nature Communications, 5, 1–10. DOI: 10.1038/ncomms5323.
   Web of Science ®Google Scholar
• Sprinzak, E., Sattath, S., and Margalit, H. (2003), “How Reliable are Experimental Protein-Protein Interaction Data?” Journal of Molecular Biology, 11, 919–23. DOI: 10.1016/s0022-2836(03)00239-0.
   Google Scholar
• Sugasawa, S. (2021), “Grouped Heterogeneous Mixture Modeling for Clustered Data,” Journal of the American Statistical Association, 116, 999–1010. DOI: 10.1080/01621459.2020.1777136.
   Web of Science ®Google Scholar
• Sun, J., Taylor, D., and Bollt, E. M. (2015), “Causal Network Inference by Optimal Causation Entropy,” SIAM Journal on Applied Dynamical Systems, 14, 73–106. DOI: 10.1137/140956166.
   Web of Science ®Google Scholar
• Wang, J., Zhang, J., Liu, B., Zhu, J., and Guo, J. (2021), “Fast Network Community Detection with Profile-Pseudo Likelihood Methods,” Journal of the American Statistical Association, 118, 1359–1372. DOI: 10.1080/01621459.2021.1996378.
   Web of Science ®Google Scholar
• Wang, Y., Joshi, T., Zhang, X.-S., Xu, D., and Chen, L. (2006), “Inferring Gene Regulatory Networks from Multiple Microarray Datasets,” Bioinformatics, 22, 2413–2420. DOI: 10.1093/bioinformatics/btl396.
   PubMed Web of Science ®Google Scholar
• Westveld, A. H., and Hoff, P. D. (2011), “A Mixed Effects Model for Longitudinal Relational and Network Data, with Applications to International Trade and Conflict,” The Annals of Applied Statistics, 5, 843–872. DOI: 10.1214/10-AOAS403.
   Web of Science ®Google Scholar
• White, H. (1982), “Maximum Likelihood Estimation of Misspecified Models,” Econometrica: Journal of the Econometric Society, 50, 1–25. DOI: 10.2307/1912526.
   Web of Science ®Google Scholar
• Yuan, Y., and Qu, A. (2021), “Community Detection with Dependent Connectivity,” The Annals of Statistics, 49, 2378–2428. DOI: 10.1214/20-AOS2042.
   Web of Science ®Google Scholar
• Zhang, W., Jin, B., and Bai, Z. (2021), “Learning Block Structures in u-statistic-based Matrices,” Biometrika, 108, 933–946. DOI: 10.1093/biomet/asaa099.
   Web of Science ®Google Scholar