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Statistical Signal Processing for Graphs


Abstract Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networ... (more)
Created Date 2015
Contributor Bliss, Nadya Travinin (Author) / Laubichler, Manfred (Advisor) / Castillo-Chavez, Carlos (Advisor) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Subject Applied mathematics
Type Doctoral Dissertation
Extent 99 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Applied Mathematics for the Life and Social Sciences 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis