Identifying network structure similarity using spectral graph theory
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Authors
Gera, Ralucca
Alonso, L.
Crawford, Brian
House, Jeffrey
Mendez-Bermudez, J.A.
Knuth, Thomas
Miller, Ryan
Subjects
Networktopology
Graphcomparisonmetrics
Laplacian
Eigenvalue distribution
Kolmogorov-Smirnov test
Graphcomparisonmetrics
Laplacian
Eigenvalue distribution
Kolmogorov-Smirnov test
Advisors
Date of Issue
2018
Date
2018
Publisher
Elsevier
Language
Abstract
Most real networks are too large or they are not available for real time analysis. Therefore, in practice, decisions are made based on partial information about the ground truth network. It is of great interest to have metrics to determine if an inferred network (the partial information network) is similar to the ground truth. In this paper we develop a test for similarity between the inferred and the true network. Our research utilizes a network visualization tool, which systematically discovers a network, producing a sequence of snapshots of the network. We introduce and test our metric on the consecutive snapshots of a network, and against the ground truth. To test the scalability of our metric we use a random matrix theory approach while discovering Erdös-Rényi graphs. This scaling analysis allows us to make predictions about the performance of the discovery process.
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Article
Description
The article of record as published may be found at http://dx.doi.org/10.1007/s41109-017-0042-3
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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.