Discovering and leveraging communities in dark multi-layered networks for network disruption
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Authors
Miller, Ryan
Gera, Ralucca
Saxena, Akrati
Chakraborty, Tanmoy
Subjects
community detection
multi-layered network
dark networks
interactive algorithm.
multi-layered network
dark networks
interactive algorithm.
Advisors
Date of Issue
2018
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
In this paper we introduce a methodology to identify communities in dark multilayered networks, taking into account that the main challenges of these networks are incompleteness, fuzzy boundaries, and dynamic behavior. To account for these characteristics, we create knowledge sharing communities (KSC) that determine the community detection. KSC is driven by weighing the edge attributes as desired for the application that the communities are used. We provide an interactive algorithm that allows the operator to decide on the weights and the thresholds applied to create the communities. By choosing these variables, our results quantitatively outperform community detection on the collapsed monoplex network.
Type
Conference Paper
Description
The article of record as published may be found at https://doi.org/10.1007/s13278-018-0520-3
Series/Report No
Department
Applied Mathematics
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
8 p.
Citation
Miller, Ryan, et al. "Discovering and leveraging communities in dark multi-layered networks for network disruption." 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018.
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.