776
Views
0
CrossRef citations to date
0
Altmetric
Theory and Methods

Network Inference Using the Hub Model and Variants

, , , , &
Pages 1264-1273 | Received 21 Oct 2020, Accepted 15 Feb 2023, Published online: 22 Mar 2023

References

  • Abbe, E. (2018), “Community Detection and Stochastic Block Models: Recent Developments,” Journal of Machine Learning Research, 18, 1–86.
  • Allman, E. S., Matias, C., and Rhodes, J. A. (2009), “Identifiability of Parameters in Latent Structure Models with Many Observed Variables,” The Annals of Statistics, 37, 3099–3132. DOI: 10.1214/09-AOS689.
  • Barabási, A.-L., and Albert, R. (1999), “Emergence of Scaling in Random Networks,” Science, 286, 509–512. DOI: 10.1126/science.286.5439.509.
  • Bickel, P., and Chen, A. (2009), “A Nonparametric View of Network Models and Newman-Girvan and Other Modularities,” Proceedings of the National Academy of Sciences, 106, 21068–21073. DOI: 10.1073/pnas.0907096106.
  • Bickel, P., Choi, D., Chang, X., and Zhang, H. (2013), “Asymptotic Normality of Maximum Likelihood and its Variational Approximation for Stochastic Blockmodels,” The Annals of Statistics, 41, 1922–1943. DOI: 10.1214/13-AOS1124.
  • Brault, V., Keribin, C., and Mariadassou, M. (2020), “Consistency and Asymptotic Normality of Latent Block Model Estimators,” Electronic Journal of Statistics, 14, 1234–1268. DOI: 10.1214/20-EJS1695.
  • Cairns, S. J., and Schwager, S. J. (1987), “A Comparison of Association Indices,” Animal Behavior, 35, 1454–1469. DOI: 10.1016/S0003-3472(87)80018-0.
  • Choi, D. S., Wolfe, P. J., and Airoldi, E. M. (2012), “Stochastic Blockmodels with Growing Number of Classes,” Biometrika, 99, 273–284. DOI: 10.1093/biomet/asr053.
  • Diaconis, P., and Janson, S. (2007), “Graph Limits and Exchangeable Random Graphs,” arXiv preprint arXiv:0712.2749.
  • Frank, O., and Strauss, D. (1986), “Markov Graphs,” Journal of the American Statistical Asscociation, 81, 832–842. DOI: 10.1080/01621459.1986.10478342.
  • Gao, C., Lu, Y., and Zhou, H. H. (2015), “Rate-Optimal Graphon Estimation,” The Annals of Statistics, 43, 2624–2652. DOI: 10.1214/15-AOS1354.
  • Getoor, L., and Diehl, C. P. (2005), “Link Mining: A Survey,” ACM SIGKDD Explorations Newsletter, 7, 3–12. DOI: 10.1145/1117454.1117456.
  • Ghalanos, A., and Theussl, S. (2015), Rsolnp: General Non-linear Optimization Using Augmented Lagrange Multiplier Method, R package version 1.16.
  • 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.
  • Gu, Y., and Xu, G. (2019a), “Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models,” Journal of Machine Learning Research, 20, 1–58.
  • Gu, Y., and Xu, G. (2019b), “The Sufficient and Necessary Condition for the Identifiability and Estimability of the Dina Model,” Psychometrika, 84, 468–483.
  • Gyllenberg, M., Koski, T., Reilink, E., and Verlaan, M. (1994), “Non-Uniqueness in Probabilistic Numerical Identification of Bacteria,” Journal of Applied Probability, 31, 542–548. DOI: 10.2307/3215044.
  • Hoff, P. D. (2007), “Modeling Homophily and Stochastic Equivalence in Symmetric Relational Data,” in Advances in Neural Information Processing Systems (Vol. 20), Curran Associates, Inc.
  • Hoff, P. D., Raftery, A. E., and Handcock, M. S. (2002), “Latent Space Approaches to Social Network Analysis,” Journal of the American Statistical Asscociation, 97, 1090–1098. DOI: 10.1198/016214502388618906.
  • Huang, T., Peng, H., and Zhang, K. (2017), “Model Selection for Gaussian Mixture Models,” Statistica Sinica, 27, 147–169. DOI: 10.5705/ss.2014.105.
  • Moreno, J. L. (1934), Who Shall Survive? A New Approach to the Problem of Human Interactions, Washington DC: Nervous and Mental Disease Publishing Co.
  • Newman, M. (2010), Networks: An Introduction, Oxford: Oxford University Press.
  • Robins, G., Pattison, P., Kalish, Y., and Lusher, D. (2007), “An Introduction to Exponential Random Graph (p*) Models for Social Networks,” Social Networks, 29, 173–191. DOI: 10.1016/j.socnet.2006.08.002.
  • Shizuka, D., and Farine, D. R. (2016), “Measuring the Robustness of Network Community Structure Using Assortativity,” Animal Behaviour, 112, 237–246. DOI: 10.1016/j.anbehav.2015.12.007.
  • Van der Vaart, A. W. (2000), Asymptotic Statistics (Vol. 3), Cambridge: Cambridge University Press.
  • Wasserman, S., and Faust, C. (1994), Social Network Analysis: Methods and Applications, Cambridge: Cambridge University Press.
  • Weko, C., and Zhao, Y. (2017), “Penalized Component Hub Models,” Social Networks, 49, 27–36. DOI: 10.1016/j.socnet.2016.09.003.
  • Xu, G. (2017), “Identifiability of Restricted Latent Class Models with Binary Responses,” The Annals of Statistics, 45, 675–707. DOI: 10.1214/16-AOS1464.
  • Zhang, Y., Levina, E., and Zhu, J. (2017), “Estimating Network Edge Probabilities by Neighbourhood Smoothing,” Biometrika, 104, 771–783. DOI: 10.1093/biomet/asx042.
  • Zhao, Y. (2017), “A Survey on Theoretical Advances of Community Detection in Networks,” Wiley Interdisciplinary Reviews: Computational Statistics, 9, e1403.
  • Zhao, Y. (2022), “Network Inference from Temporally Dependent Grouped Observations,” Computational Statistics & Data Analysis, 171, 107470.
  • Zhao, Y., and Weko, C. (2019), “Network Inference from Grouped Observations using Hub Models,” Statistica Sinica, 29, 225–244. DOI: 10.5705/ss.202016.0397.

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.