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The performance of spectral clustering can be considerably improved via regularization, as demonstrated empirically in Amini et.
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Fitting community models to large sparse networks
A. Chen, A. Amini, P. Bickel, and L. Levina · 2012
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Matrix concentration inequalities via the method of exchangeable pairs
L. Mackey, M.I. Jordan, R.Y. Chen, B. Farrell, and J.A. Tropp · 2012
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Daniel L Sussman, Minh Tang, Donniell E Fishkind, and Carey E Priebe · 2012
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Pseudo-likelihood methods for community detection in large sparse networks
A.A. Amini, A. Chen, P.J. Bickel, and E. Levina · 2013
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K. Rohe, S. Chatterjee, and B. Yu · 2011
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Improved spectral-norm bounds for clustering
Pranjal Awasthi and Or Sheffet · 2012
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Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown
Donniell E Fishkind, Daniel L Sussman, Minh Tang, Joshua T Vogelstein, and Carey E Priebe · 2013
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Fast sparse superposition codes have near exponential error probability for R < C R<C
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Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Shayan Oveis Gharan, and Luca Trevisan · 2013
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Tai Qin and Karl Rohe · 2013
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