Dissertation
Feature extraction from network data
Doctor of Philosophy (PhD), Washington State University
01/2019
Handle:
https://hdl.handle.net/2376/17893
Abstract
This work explores different approaches to feature extraction from network data. The first part focuses on Boolean networks, a simplistic discrete dynamical system built over graphs. We propose a method for statistical analysis of attractors of a Boolean network, and use it to learn about its behavior patterns at the system level. The analysis aggregates information about the direct interactions between the modeled objects, encoded in a Boolean network model. In the second part, we regard graph matching, a fundamental problem in the field of machine intelligence. We introduce an approach to analyze graph data based on its representation as a metric space. To measure shape difference in graphs, we develop and implement a polynomial-time algorithm for estimating one of the Gromov–Hausdorff distances.
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Details
- Title
- Feature extraction from network data
- Creators
- Vladyslav Oles
- Contributors
- Alexander Panchenko (Advisor)Kevin Vixie (Committee Member)Kevin Cooper (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Department of Mathematics and Statistics
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Number of pages
- 91
- Identifiers
- 99900581414801842
- Language
- English
- Resource Type
- Dissertation