Description
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 networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.
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Details
Title
- Statistical signal processing for graphs
Contributors
- Bliss, Nadya Travinin (Author)
- Laubichler, Manfred (Thesis advisor)
- Castillo-Chavez, Carlos (Thesis advisor)
- Papandreou-Suppappola, Antonia (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2015
Subjects
Resource Type
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Note
- thesisPartial requirement for: Ph.D., Arizona State University, 2015
- bibliographyIncludes bibliographical references (pages 84-89)
- Field of study: Applied mathematics for the life and social sciences
Citation and reuse
Statement of Responsibility
by Nadya Travinin Bliss