Degree ranking using local information
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Authors
Saxena, Akrati
Gera, Ralucca
Iyengar, S.R.S.
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Date of Issue
2017-06-10
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Abstract
Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only 1% samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for random networks and validate their efficiency on synthetic random networks, that are generated using Erdös-Rényi model. Results show that the proposed methods can be efficiently used for random networks as well.
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Article
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Department
Applied Mathematics
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Naval Postgraduate School (U.S.)
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39 p.
Citation
A. Saxena, R. Gera, S.R.S., "Degree ranking using local information," arXiv:1706.01205v2 [cs.SI] 10 Jun 2017.
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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.