Open Access
Issue
ESAIM: PS
Volume 27, 2023
Page(s) 80 - 135
DOI https://doi.org/10.1051/ps/2022019
Published online 06 January 2023
  1. E. Abbe, Community detection and stochastic block models: recent developments. J. Mach. Learning Res. 18 (2017) 1-86. [Google Scholar]
  2. E. Abbe and C. Sandon, Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery, in 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, IEEE (2015). [Google Scholar]
  3. E. Abbe and C. Sandon, Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation (2016). [Google Scholar]
  4. L.A. Adamic and E. Adar, Friends and neighbors on the Web. Social Netw. 25 (2003) 211-230. [Google Scholar]
  5. R. Ahmad and K.S. Xu, Effects of contact network models on stochastic epidemic simulations, in International Conference on Social Informatics. Springer (2017). [Google Scholar]
  6. E. Armengol et al., Evaluating link prediction on large graphs, in Artificial intelligence research and development: proceedings of the 18th international conference of the Catalan association for artificial intelligence, vol. 277 (2015). [Google Scholar]
  7. A.-L. Barabási, Scale-free networks: a decade and beyond. Science 325 (2009) 412-413. [Google Scholar]
  8. L.E. Baum and T. Petrie, Statistical inference for probabilistic functions of finite state Markov chains, Ann. Math. Stat. 37 (1966) 1554-1563. [CrossRef] [Google Scholar]
  9. Q. Berthet and N. Baldin, Statistical and Computational Rates in Graph Logistic Regression, in S. Chiappa and R. Calandra (editors), Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 108, PMLR (2020) 2719-2730. [Google Scholar]
  10. F. Bickenbach, E. Bode et al., Markov or not Markov-This should be a question, Tech. rep., Kiel working paper (2001). [Google Scholar]
  11. K.P. Burnham and D.R. Anderson, Multimodel inference: understanding AIC and BIC in model selection. Sociolog. Methods Res. 33 (2004) 261-304. [Google Scholar]
  12. O. Cappá, E. Moulines and T. Rydán, Inference in Hidden Markov Models, in Proceedings of EUSFLAT conference (2009) 14-16. [Google Scholar]
  13. A. Celisse, J.-J. Daudin and L. Pierre, Consistency of maximum-likelihood and variational estimators in the stochastic block model. Electr. J. Stat. 6 (2012) 1847-1899. [Google Scholar]
  14. A. Celisse, J.-J. Daudin and L. Pierre, Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model. Electr. J. Stat. 6 (2012) 1847-1899. [Google Scholar]
  15. M. Charikar, S. Guha, E. Tardos and D. Shmoys, A constant-factor approximation algorithm for the k-median problem. J. Comput. Syst. Sci. 65 (2002) 129-149. [CrossRef] [Google Scholar]
  16. Y. Chen and J. Xu, Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices (2014). [Google Scholar]
  17. Z. Chen and S. Wang, A review on matrix completion for recommender systems. Knowl. Inf. Syst. 64 (2022) 1-34. [Google Scholar]
  18. S. Chin, A. Rao and V. Vu, Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery (2015). [Google Scholar]
  19. G.A. Churchill, Stochastic models for heterogeneous DNA sequences. Bull. Math. Biol. 51 (1989) 79-94. [CrossRef] [MathSciNet] [Google Scholar]
  20. A. Clauset, C. Moore and M.E.J. Newman, Hierarchical structure and the prediction of missing links in networks. Nature 453 (2008) 98-101. [Google Scholar]
  21. J.C. Costello, L.M. Heiser, E. Georgii, M. Gonen, M.P. Menden, N.J. Wang, M. Bansal, M. Ammad-Ud-Din, P. Hintsanen, S.A. Khan et al., A community effort to assess and improve drug sensitivity prediction algorithms, Nat. Biotechnol. 32 (2014) 1202-1212. [Google Scholar]
  22. S. Das and S.K. Das, A probabilistic link prediction model in time-varying social networks, in 2017 IEEE International Conference on Communications (ICC) (2017) 1-6. [Google Scholar]
  23. J.-J. Daudin, F. Picard and S. Robin, A mixture model for random graphs. Stat. Comput. 18 (2008) 173-183. [Google Scholar]
  24. A. Divakaran and A. Mohan, Temporal link prediction: a survey. New Generat. Comput. 38 (2020) 213-258. [Google Scholar]
  25. Q. Duchemin and Y. De Castro, Markov random geometric graph, MRGG: a growth model for temporal dynamic networks. Electr. J. Stat. 16 (2022) 671-699. [Google Scholar]
  26. Y. Fei and Y. Chen, Exponential error rates of SDP for block models: beyond Grothendieck’s inequality. IEEE Trans. Inf. Theory PP (2017). [Google Scholar]
  27. X. Feng, J. Zhao and K. Xu, Link prediction in complex networks: a clustering perspective. Eur. Phys. J. B 85 (2012) 1-9. [CrossRef] [Google Scholar]
  28. D.R. Fredkin and J.A. Rice, Correlation functions of a function of a finite-state Markov process with application to channel kinetics. Math. Biosci. 87 (1987) 161-172. [Google Scholar]
  29. C. Giraud and N. Verzelen, Partial recovery bounds for clustering with the relaxed K-means. Math. Stat. Learn. 1 (2019) 317-374. [Google Scholar]
  30. O. Guédon and R. Vershynin, Community detection in sparse networks via Grothendieck’s inequality. Probab. Theory Related Fields 165 (2014). [Google Scholar]
  31. R. Guimera and M. Sales-Pardo, Missing and spurious interactions and the reconstruction of complex networks. Proc. Natl. Acad. Sci. 106 (2009) 22073-22078. [Google Scholar]
  32. B. Hajek, Y. Wu and J. Xu, Semidefìnite Programs for Exact Recovery of a Hidden Community (2016). [Google Scholar]
  33. G.-J. Huizing, G. Peyré and L. Cantini, Optimal Transport improves cell-cell similarity inference in single-cell omics data. bioRxiv (2021). [Google Scholar]
  34. B. Jiang, Q. Sun and J. Fan, Bernstein’s inequality for general Markov chains. arXiv preprint abs/arXiv:1805.10721 (2018). [Google Scholar]
  35. B.H. Juang and L.R. Rabiner, Hidden Markov models for speech recognition. Technometrics 33 (1991) 251-272. [Google Scholar]
  36. B. Karrer and M. Newman, Stochastic blockmodels and community structure in networks. Phys. Rev. E 83 (2011). [Google Scholar]
  37. N. Keriven and S. Vaiter, Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model (2020). [Google Scholar]
  38. A. Kölzsch, E. Kleyheeg, H. Kruckenberg, M. Kaatz and B. Blasius, A periodic Markov model to formalize animal migration on a network. Royal Soc. Open Sci. 5 (2018) 180438. [Google Scholar]
  39. H. Kruckenberg, G. Muöskens and B. Ebbinge, Data from: a periodic Markov model to formalise animal migration on a network [white-fronted goose data] (2018). [Google Scholar]
  40. A. Kumar, S.S. Singh, K. Singh and B. Biswas, Link prediction techniques, applications, and performance: a survey. Physica A 553 (2020) 124289. [Google Scholar]
  41. R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tompkins and E. Upfal, The Web as a Graph, in Proceedings of the Nineteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS ‘00, Association for Computing Machinery, New York, NY, USA (2000) 1-10. [Google Scholar]
  42. P. Latouche, E. Birmele and C. Ambroise, Variational Bayesian inference and complexity control for stochastic block models. Stat. Modell. 12 (2012) 93-115. [Google Scholar]
  43. J. Lei and A. Rinaldo, Consistency of spectral clustering in stochastic block models. Ann. Stat. 43 (2015). [Google Scholar]
  44. Levin, Markov chains and mixing times, American Mathematical Soc. (2017). [Google Scholar]
  45. J.H. Levine, E.F. Simonds, S.C. Bendall, K.L. Davis, D.A. El-ad, M.D. Tadmor, O. Litvin, H.G. Fienberg, A. Jager, E.R. Zunder et al., Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162 (2015) 184-197. [CrossRef] [PubMed] [Google Scholar]
  46. X. Li, N. Du, H. Li, K. Li, J. Gao and A. Zhang, A deep learning approach to link prediction in dynamic networks, in Proceedings of the 2014 SIAM International conference on data mining. SIAM (2014) 289-297. [Google Scholar]
  47. X. Ma, P. Sun and Y. Wang, Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks. Physica A 496 (2018) 121-136. [Google Scholar]
  48. M. Mariadassou, S. Robin and C. Vacher, Uncovering latent structure in valued graphs: a variational approach. Ann. Appl. Stat. 4 (2010) 715-742. [CrossRef] [MathSciNet] [Google Scholar]
  49. C. Matias and V. Miele, Statistical clustering of temporal networks through a dynamic stochastic block model. J. Roy. Stat. Soc.: Ser. B (Statistical Methodology) 79 (2015) 1119-1141. [Google Scholar]
  50. S.A. Morris and M.L. Goldstein, Manifestation of research teams in journal literature: a growth model of papers, authors, collaboration, coauthorship, weak ties, and Lotka’s law. J. Assoc. Inf. Sci. Technol. 58 (2007) 1764-1782. [CrossRef] [Google Scholar]
  51. M.E. Newman, Clustering and preferential attachment in growing networks. Phys. Rev. E 64 (2001) 025102. [Google Scholar]
  52. M. Opper and D. Saad, Advanced Mean Field Methods: Theory and Practice. The MIT Press (2001). [Google Scholar]
  53. J. Peng and Y. Wei, Approximating K-means-type clustering via semidefìnite programming. SIAM J. Optim. 18 (2007) 186-205. [Google Scholar]
  54. M. Pensky and T. Zhang, Spectral clustering in the dynamic stochastic block model. Electr. J. Stat. 13 (2017) 678-709. [Google Scholar]
  55. W. Perry and A.S. Wein, A semidefìnite program for unbalanced multisection in the stochastic block model (2015). [Google Scholar]
  56. P. Sarkar, D. Chakrabarti and M. Jordan, Nonparametric link prediction in dynamic networks. arXiv preprint arXiv:1206.6394 (2012). [Google Scholar]
  57. R.R. Sarukkai, Link prediction and path analysis using Markov chains. Comput. Netw. 33 (2000) 377-386. [CrossRef] [Google Scholar]
  58. O. Shchur and S. Guönnemann, Overlapping Community Detection with Graph Neural Networks (2019). [Google Scholar]
  59. A.L. Smith, D.M. Asta and C.A. Calder, The geometry of continuous latent space models for network data. Statist. Sci. 34 (2019) 428-453. [Google Scholar]
  60. P. Wang, B. Xu, Y. Wu and X. Zhou, Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58 (2015) 1-38. [Google Scholar]
  61. D.J. Watts and S.H. Strogatz, Collective dynamics of ‘small-world’ networks. Nature 393 (1998) 440-442. [Google Scholar]
  62. R. Weiss and B. Nadler, Learning parametric-output HMMs with two aliased states, in F. Bach and D. Blei (editors), Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 37, PMLR, Lille, France (2015) 635-644. [Google Scholar]
  63. Z. Wu and Y. Chen, Link prediction using matrix factorization with bagging, in 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), IEEE (2016) 1-6. [Google Scholar]
  64. K.S. Xu, Stochastic block transition models for dynamic networks. CoRR (2014). [Google Scholar]
  65. M. Yang, J. Simm, C.C. Lam, P. Zakeri, G.J. van Westen, Y. Moreau and J. Saez-Rodriguez, Linking drug target and pathway activation for effective therapy using multi-task learning. Sci. Rep. 8 (2018) 1-10. [Google Scholar]
  66. T. Yang, Y. Chi, S. Zhu, Y. Gong and R. Jin, Detecting communities and their evolutions in dynamic social networks - a Bayesian approach. Mach. Learn. 82 (2011) 157-189. [Google Scholar]
  67. W. Yuan, K. He, D. Guan, L. Zhou and C. Li, Graph kernel based link prediction for signed social networks. Inf. Fusion 46 (2019) 1-10. [CrossRef] [Google Scholar]
  68. X. Zhang, X. Wang, C. Zhao, D. Yi and Z. Xie, Degree-corrected stochastic block models and reliability in networks. Physica A 393 (2014) 553-559. [Google Scholar]
  69. T. Zhou, L. Lu and Y.-C. Zhang, Predicting missing links via local Information. Eur. Phys. J. B 71 (2009) 623-630. [CrossRef] [Google Scholar]

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