Network Similarity using Distribution of Distance Matrices
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Authors
Gera, Ralucca
Yoshida, Ruriko
Subjects
Advisors
Date of Issue
2018
Date
Publisher
Monterey, California. Naval Postgraduate School
Monterey, California. Naval Postgraduate School
Monterey, California. Naval Postgraduate School
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Abstract
Decision makers use partial information of networks to guide their decision, yet when they act, they act in the real network or the ground truth. Therefore, a way of comparing the partial information to ground truth is required. We introduce a statistical measure that analyzes the network obtained from the partially observed information and ground truth, which of course can be applied to the comparison of any networks. As a first step, in the current research, we restrict ourselves to networks of the same size to introduce such a method, which can be generalized to different size networks. We perform mathematical analysis on the random graph, and then apply our methodology to synthetic networks generated using five different generating models. We conclude with a statistical hypothesis test to decide whether two graphs are correlated or not correlated.
Type
Preprint
Description
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Series/Report No
Department
Applied Mathematics
Organization
Naval Postgraduate School (U.S.)
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Format
8 p.
Citation
Gera, Ralucca, and Ruriko Yoshida. "Network Similarity Using Distribution of Distance Matrices." 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018.
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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.