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.
|Commitee:||Vixie, Kevin, Cooper, Kevin|
|School:||Washington State University|
|School Location:||United States -- Washington|
|Source:||DAI-B 81/9(E), Dissertation Abstracts International|
|Keywords:||Anomaly detection, Boolean networks, Computational geometry, Graph matching, Gromov–Hausdorff distances, Shape analysis|
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