Blind Network Inference

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dc.contributor.advisorSegarra, Santiago
dc.creatorRoddenberry, Thomas M
dc.date.accessioned2021-08-16T19:29:59Z
dc.date.available2021-08-16T19:29:59Z
dc.date.created2021-08
dc.date.issued2021-08-12
dc.date.submittedAugust 2021
dc.date.updated2021-08-16T19:29:59Z
dc.description.abstractWe consider the extraction of “coarse” descriptions of networks strictly from observing data supported on their nodes. Taking a graph signal processing perspective, we model the observed data as the output of a graph filter applied to white noise. We then consider two tasks. First, we infer the eigenvector centrality ranking of the underlying graph, drawing connections between network diffusion processes, graph filters, and the power method for eigenvector computation. In doing so, we derive statistical guarantees for correctly comparing pairs of nodes in terms of their eigenvector centrality ranking from a simple PCA-type procedure. Second, we extract the community structure of a collection of planted partition graphs over a shared set of nodes from nodal data. In this case, we show that the optimal conditions for centrality ranking are suboptimal for community detection. We also derive statistical guarantees for estimating the number of communities in the underlying planted partition model as well as exactly inferring the community membership structure.
dc.format.mimetypeapplication/pdf
dc.identifier.citationRoddenberry, Thomas M. "Blind Network Inference." (2021) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/111200">https://hdl.handle.net/1911/111200</a>.
dc.identifier.urihttps://hdl.handle.net/1911/111200
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectGraph signal processing
dc.subjectclustering
dc.subjectsystem identification
dc.subjectcentrality
dc.subjectnetworks
dc.titleBlind Network Inference
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.majorData Science
thesis.degree.nameMaster of Science
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