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most citedSpectral redemption: clustering sparse networks

658 citations · 2.2k across the 187 of their papers we have counts for

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Showing 2013Show all

7 papers · 1 filter

math.PR2013

Mixing under monotone censoring

Jian Ding, Elchanan Mossel

We initiate the study of mixing times of Markov chain under monotone censoring. Suppose we have some Markov Chain on a state space with stationary distribution and a mo…

math.PR2013

A Proof Of The Block Model Threshold Conjecture

Elchanan Mossel, Joe Neeman, Allan Sly

We study a random graph model named the "block model" in statistics and the "planted partition model" in theoretical computer science. In its simplest form, this is a random graph…

math.PR2013★ 43 cited

Belief propagation, robust reconstruction and optimal recovery of block models

Elchanan Mossel, Joe Neeman, Allan Sly

We consider the problem of reconstructing sparse symmetric block models with two blocks and connection probabilities and for inter- and intra-block edge probabilities,…

cs.LG2013

MCMC Learning

Varun Kanade, Elchanan Mossel

The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a…

physics.soc-ph2013★ 19 cited

Coexistence in preferential attachment networks

Tonći Antunović, Elchanan Mossel, Miklos Z. Racz

We introduce a new model of competition on growing networks. This extends the preferential attachment model, with the key property that node choices evolve simultaneously with the…

cs.CC2013

Computation in anonymous networks

Elchanan Mossel, Anupam Prakash, Gregory Valiant

We identify and investigate a computational model arising in molecular computing, social computing and sensor network. The model is made of of multiple agents who are computational…

cs.SI2013★ 658 cited

Spectral redemption: clustering sparse networks

Florent Krzakala, Cristopher Moore, Elchanan Mossel +4

Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal,…