Skip navigation
Please use this identifier to cite or link to this item:
Title: Replicated Random Graphs under a Mixture of Low-Rank Decompositions with applications to Multiview Network Modeling
Authors: Parmar, Viraj Vijay
Advisors: Engelhardt, Barbara
Contributors: McConnell, Mark
Department: Mathematics
Class Year: 2016
Abstract: Collections of network-valued data are prevalent in many scientific domains. Popular methods for analysis use averaging techniques to study statistical properties of the collection, leading to the possible loss of information or structure from the underlying distribution. In this paper we investigate a probabilistic framework for inferring global similarities and local deviations in a set of observed networks generated by a common random variable. This approach leverages a recently developed nonparametric Bayesian random graph model using a mixture of low-rank decompositions in order to facilitate both dimensionality reduction and clustering. We formulate the model and derive a Gibbs sampling procedure for posterior inference. Furthermore, we demonstrate a novel application for unsupervised learning in multivew networks.
Extent: 25 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Mathematics, 1934-2020

Files in This Item:
File SizeFormat 
Parmar_Viraj_thesis.pdf792.2 kBAdobe PDF    Request a copy

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