A METHOD FOR CLASSIFICATION OF INCOMPLETE NETWORKS: TRAINING THE MODEL WITH COMPLETE AND INCOMPLETE INFORMATION
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Authors
Vu, Carolyne
Subjects
network analysis
incomplete information
network classification
graph theory
incomplete information
network classification
graph theory
Advisors
Yoshida, Ruriko
Date of Issue
2019-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The rise of accessible real-world data creates a growing interest in effective methods for accurate classification, especially for networks with incomplete information. The intelligence community requires an understanding of a network before the team can develop a strategy to combat the adversary. These problems are typically time-sensitive; however, gathering complete and actionable intelligence is a challenging mission. An adversary’s actions are secretive in nature. Crucial information is deliberately concealed. Intentionally dubious information creates problematic noise. Therefore, if an observed incomplete network can be classified as-is without delay, the network can be properly analyzed for a strategy to be devised and acted upon earlier. This thesis considers a machine learning technique for classification of incomplete networks. We examine the effects of training the model with complete and incomplete information. Observed network data and their structural features are classified into technological, social, information, and biological categories using supervised learning methods.
Type
Thesis
Description
Student Thesis (NPS NRP Project Related)
Department
Operations Research (OR)
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
Sponsors
Office of Naval Intelligence (ONI)
Funder
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Chief of Naval Operations (CNO)
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.