Partial information community detection in a multilayer network
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Authors
Warnke, Scott D.
Subjects
multilayer networks
partial information
network discovery
synthetic network construction
community detec-tion
dark networks
partial information
network discovery
synthetic network construction
community detec-tion
dark networks
Advisors
Gera, Ralucca
Date of Issue
2016-06
Date
Jun-16
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Identifying communities in a dark network is a potentially difficult task. The nature of dark networks, and their characteristic of con-cealing connections within the network, makes community detection an enterprise based on operations and decisions with only partial information. We take this concept of operation with only partial information, and extend it to our work by identifying communities within a dark network using only a single layer from the full multilayer network. Additionally, the concept of identification of terrorist networks within civilian populations is one of ever-increasing importance in our world today. We create a large multilayer synthetic network, and embed a known terrorist network in the larger synthetic network. We construct our synthetic network in a manner to ensure that our terrorist network is not unique, in order to make discovery of the terrorist network difficult. In this portion of our work we are concerned with identifying the entire terrorist network, not just a community within the terrorist network. We use known discovery algorithms to discover the terrorist network, and compare the results to modified algorithms introduced in this thesis and their ability to discover the terrorist network as quickly as possible.
Type
Thesis
Description
Series/Report No
Department
Applied Mathematics
Organization
Identifiers
NPS Report Number
Sponsors
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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.