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Paper Abstract and Keywords
Presentation 2025-12-11 14:00
Automated estimation of scaling regions and power-law exponents for degree distributions of scale-free networks
Shuta Goto, Kazuya Sawada, Tohru Ikeguchi (Tokyo Univ. of Sci.) NLP2025-62
Abstract (in Japanese) (See Japanese page) 
(in English) Power-law behavior can be observed in various fields of natural and social sciences.
Typical examples include the magnitude-frequency distribution of earthquakes, income distributions, and the relationship between word rank and word frequency in natural languages.
Thus, several methods have already been proposed to extract the scaling region and estimate the power-law exponent from empirical distributions.
Among them, Deshmukh et al proposed an automated method for estimating the exponent and the scaling region.
However, this method suffers from long computation times when the data points in the distribution are densely concentrated.
In this article, we propose a method that can quickly extract the scaling region and estimate the power-law exponent, even when the point density within the distribution is high.
In numerical experiments, the proposed method was applied to the degree distributions of the Barabási–Albert model, in which the density of data points varies locally.
The results show that our method reduces the computation time to less than one-tenth of that required by the method by Deshmukh et al., while also yielding smaller confidence intervals for the estimated exponent.
Furthermore, the proposed method resolves a key issue in the conventional method, namely, the inconsistency between the estimated power-law exponent and the identified scaling region.
In addition, we found that a huge number of vertices is required to accurately estimate
the power-law exponent of the degree distribution obtained from the BA model.
Keyword (in Japanese) (See Japanese page) 
(in English) Power-law / Scaling region / Power-law exponent / Barabási-Albert model / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 283, NLP2025-62, pp. 48-53, Dec. 2025.
Paper # NLP2025-62 
Date of Issue 2025-12-04 (NLP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLP2025-62

Conference Information
Committee NLP  
Conference Date 2025-12-11 - 2025-12-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Kochi Castle Museum of History 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear problem, etc 
Paper Information
Registration To NLP 
Conference Code 2025-12-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automated estimation of scaling regions and power-law exponents for degree distributions of scale-free networks 
Sub Title (in English)  
Keyword(1) Power-law  
Keyword(2) Scaling region  
Keyword(3) Power-law exponent  
Keyword(4) Barabási-Albert model  
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1st Author's Name Shuta Goto  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Sci.)
2nd Author's Name Kazuya Sawada  
2nd Author's Affiliation Tokyo University of Science (Tokyo Univ. of Sci.)
3rd Author's Name Tohru Ikeguchi  
3rd Author's Affiliation Tokyo University of Science (Tokyo Univ. of Sci.)
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Speaker Author-2 
Date Time 2025-12-11 14:00:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # NLP2025-62 
Volume (vol) vol.125 
Number (no) no.283 
Page pp.48-53 
#Pages 6 
Date of Issue 2025-12-04 (NLP) 

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