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The stochastic block model is a popular tool for studying community structures in network data.
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Adamic, Lada A.L. A. andGlance, NatalieN. (2005). The political blogosphere and the 2004 US election: Divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery 36–43. ACM, New York
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Newman, Mark EJM. E. andGirvan, MichelleM. (2004). Finding and evaluating community structure in networks. Phys. Rev. E (3) 69 026113
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Newman, Mark EJM. E. (2006). Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103 8577–8582
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Bickel, Peter J.P. J. andChen, AiyouA. (2009). A nonparametric view of network models and Newman–Girvan and other modularities. Proc. Natl. Acad. Sci. USA 106 21068–21073
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Decelle, AurelienA., Krzakala, FlorentF., Moore, CristopherC. andZdeborová, LenkaL. (2011). Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. Phys. Rev. E (3) 84 066106
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Karrer, BrianB. andNewman, M. E. J.M. E. J. (2011). Stochastic blockmodels and community structure in networks. Phys. Rev. E (3) 83 016107, 10
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Zhao, YunpengY., Levina, ElizavetaE. andZhu, JiJ. (2011). Community extraction for social networks. Proc. Natl. Acad. Sci. USA 108 7321–7326
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Chaudhuri, KamalikaK., Chung, FanF. andTsiatas, AlexanderA. (2012). Spectral clustering of graphs with general degrees in the extended planted partition model. J. Mach. Learn. Res. Workshop Conf. Proc. 2012 35.1–35.23
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Chen, YudongY., Sanghavi, SujayS. andXu, HuanH. (2012). Clustering sparse graphs. In Advances in Neural Information Processing Systems 25 (F.F. Pereira, C. J. C.C. J. C. Burges, L.L. Bottou andK. Q.K. Q. Weinberger, eds.) 2204–2212. Curran Associates, Red Hook, NY
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Erdős, LászlóL., Yau, Horng-TzerH.-T. andYin, JunJ. (2012). Rigidity of eigenvalues of generalized Wigner matrices. Adv. Math. 229 1435–1515
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Erdős, LászlóL., Knowles, AnttiA., Yau, Horng-TzerH.-T. andYin, JunJ. (2012). Spectral statistics of Erdős-Rényi Graphs II: Eigenvalue spacing and the extreme eigenvalues. Comm. Math. Phys. 314 587–640
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Jin, JiashunJ. (2012). Fast community detection by SCORE. Available at \arxivurl
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Zhao, YunpengY., Levina, ElizavetaE. andZhu, JiJ. (2012). Consistency of community detection in networks under degree-corrected stochastic block models. Ann. Statist. 40 2266–2292
2012
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2013
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Anandkumar, AnimashreeA., Ge, RongR., Hsu, DanielD. andKakade, Sham M.S. M. (2014). A tensor approach to learning mixed membership community models. J. Mach. Learn. Res. 15 2239–2312
2014
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Bloemendal, AlexA., Erdős, LászlóL., Knowles, AnttiA., Yau, Horng-TzerH.-T. andYin, JunJ. (2014). Isotropic local laws for sample covariance and generalized Wigner matrices. Electron. J. Probab. 19 1–53
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2014
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Fishkind, Donniell E.D. E., Sussman, Daniel L.D. L., Tang, MinhM., Vogelstein, Joshua T.J. T. andPriebe, Carey E.C. E. (2013). Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown. SIAM J. Matrix Anal. Appl. 34 23–39
2013
Cited alongside, same era.
Krzakala, FlorentF., Moore, CristopherC., Mossel, ElchananE., Neeman, JoeJ., Sly, AllanA., Zdeborová, LenkaL. andZhang, PanP. (2013). Spectral redemption in clustering sparse networks. Proc. Natl. Acad. Sci. USA 110 20935–20940
2013
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2013
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2013
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2013
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2014
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Airoldi, Edoardo M.E. M., Blei, David M.D. M., Fienberg, Stephen E.S. E. andXing, Eric P.E. P. (2008). Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9 1981–2014
2014
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2014
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Lee, Ji OonJ. O. andYin, JunJ. (2014). A necessary and sufficient condition for edge universality of Wigner matrices. Duke Math. J. 163 117–173
2014
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Yan, XiaoranX., Shalizi, CosmaC., Jensen, Jacob E.J. E., Krzakala, FlorentF., Moore, CristopherC., Zdeborová, LenkaL., Zhang, PanP. andZhu, YaojiaY. (2014). Model selection for degree-corrected block models. J. Stat. Mech. Theory Exp. 2014 P05007
2014
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Chatterjee, SouravS. (2015). Matrix estimation by universal singular value thresholding. Ann. Statist. 43 177–214
2015
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