Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01pv63g3347
Title: Optimal Reconstruction of General Sparse Stochastic Block Models
Authors: Chin, Byron
Advisors: Sly, Allan
Department: Mathematics
Class Year: 2021
Abstract: This paper is motivated by the reconstruction problem on the sparse stochastic block model. The paper “Belief Propagation, Robust Reconstruction and Optimal Recovery of Block Models” by Mossel, Neeman, and Sly provided and proved a reconstruction algorithm that recovers an optimal fraction of the communities in the symmetric, 2-community case. The main contribution of their proof is to show that when the signal to noise ratio is sufficiently large, in particular λ^2d > C, the reconstruction accuracy on a tree with or without noise on the leaves is asymptotically the same. This paper will generalize their results, including the main step, to a general class of the sparse stochastic block model with any number of communities that are not necessarily symmetric, proving that an algorithm closely related to Belief Propagation recovers an optimal fraction of community labels.
URI: http://arks.princeton.edu/ark:/88435/dsp01pv63g3347
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Mathematics, 1934-2021

Files in This Item:
File Description SizeFormat 
CHIN-BYRON-THESIS.pdf532.43 kBAdobe PDF    Request a copy


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.