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Modern applications in statistics, computer science and network science have seen tremendous values of finer matrix spectral perturbation theory.
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Improved algorithms for the random cluster graph model
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A nonparametric view of network models and newman–girvan and other modularities
Peter J Bickel and Aiyou Chen · 2009
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Exact matrix completion via convex optimization
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Entrywise bounds for eigenvectors of random graphs
Pradipta Mitra · 2009
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Concentration of the adjacency matrix and of the laplacian in random graphs with independent edges
Roberto Imbuzeiro Oliveira · 2009
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Graph partitioning via adaptive spectral techniques
Amin Coja-Oghlan · 2010
Estimating the number of communities in networks by spectral methods
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Consistency of spectral clustering in stochastic block models
Jing Lei and Alessandro Rinaldo · 2015
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Sharp nonasymptotic bounds on the norm of random matrices with independent entries
Afonso S Bandeira and Ramon Van Handel · 2016
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Community detection in sparse networks via grothendieck’s inequality
Olivier Guédon and Roman Vershynin · 2016
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Achieving exact cluster recovery threshold via semidefinite programming: Extensions
Bruce Hajek, Yihong Wu, and Jiaming Xu · 2016
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Impact of regularization on spectral clustering
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
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Noise thresholds for spectral clustering
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, and Aarti Singh · 2011
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Finding dense clusters via” low rank+ sparse” decomposition
Samet Oymak and Babak Hassibi · 2011
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Spectral clustering and the high-dimensional stochastic blockmodel
Karl Rohe, Sourav Chatterjee, Bin Yu, et al · 2011
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Stochastic blockmodels with a growing number of classes
David S Choi, Patrick J Wolfe, and Edoardo M Airoldi · 2012
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Concentration inequalities in locally dependent spaces
Daniel Paulin · 2012
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Antony Joseph and Bin Yu · 2016
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Optimal cluster recovery in the labeled stochastic block model
Se-Young Yun and Alexandre Proutiere · 2016
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Community detection and stochastic block models: recent developments
Emmanuel Abbe · 2017
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Entrywise eigenvector analysis of random matrices with low expected rank
Emmanuel Abbe, Jianqing Fan, Kaizheng Wang, and Yiqiao Zhong · 2017
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Multisection in the stochastic block model using semidefinite programming
Naman Agarwal, Afonso S Bandeira, Konstantinos Koiliaris, and Alexandra Kolla · 2017
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Unperturbed: spectral analysis beyond davis-kahan
Justin Eldridge, Mikhail Belkin, and Yusu Wang · 2017
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Achieving optimal misclassification proportion in stochastic block models
Chao Gao, Zongming Ma, Anderson Y Zhang, and Harrison H Zhou · 2017
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Estimating mixed memberships with sharp eigenvector deviations
Xueyu Mao, Purnamrita Sarkar, and Deepayan Chakrabarti · 2017
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On semidefinite relaxations for the block model
Arash A Amini and Elizaveta Levina · 2018
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Random laplacian matrices and convex relaxations
Afonso S Bandeira · 2018
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Convexified modularity maximization for degree-corrected stochastic block models
Yudong Chen, Xiaodong Li, Jiaming Xu, et al · 2018
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An ℓ ∞ \ell_{\infty} eigenvector perturbation bound and its application to robust covariance estimation
Jianqing Fan, Weichen Wang, and Yiqiao Zhong · 2018
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Exponential error rates of sdp for block models: Beyond grothendieck’s inequality
Yingjie Fei and Yudong Chen · 2018
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The dimension-free structure of nonhomogeneous random matrices
Rafał Latała, Ramon van Handel, and Pierre Youssef · 2018
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Concentration of the spectral norm of erdös-rényi random graphs
Gábor Lugosi, Shahar Mendelson, and Nikita Zhivotovskiy · 2018
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A simple svd algorithm for finding hidden partitions
Van Vu · 2018
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Near-optimal bounds for phase synchronization
Yiqiao Zhong and Nicolas Boumal · 2018
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Uniform bounds for invariant subspace perturbations
Anil Damle and Yuekai Sun · 2019
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Strong consistency of spectral clustering for stochastic block models
Liangjun Su, Wuyi Wang, and Yichong Zhang · 2019
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