Extracting Information Based on Partial or Complete Network Data
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Authors
Carbaugh, James
Fletcher, Matthew
Gera, Ralucca
Lee, Woei Chieh
Nelson, Russell
Debnath, oyati
Subjects
Advisors
Date of Issue
2019
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
General frameworks that works for all types of networks usually do not produce reliable results. The frame- work in question here is extracting information from large unknown networks encountered in the real world. Generally, researchers and operators work with incomplete data. Under- standing and particularly measuring a network’s structure is a complex problem, and there is no general reliable way of measuring the structure in order to compare networks. In this research we present heuristic methods to gather information from a given network. We introduce an algorithm to place those monitors according to betweenness, closeness, degree and a new centrality called 2-hop centrality.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.30534/ijatcse/2019/5081.12019
Series/Report No
Department
Applied Mathematics
Organization
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NPS Report Number
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Funding
Format
7 p.
Citation
Matthew Fletcher, Ralucca Gera, Woei Chieh Lee, Russell Nelson, and Joyati Debnath. "Extracting information based on partial or complete network data." International Journal of Advanced Trends in Computer Science and Engineering, Volume 8, No.1.1, 2019
Distribution Statement
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.