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Paper Abstract and Keywords
Presentation 2017-10-06 13:00
Derivation of Global Clustering Coefficient Maximizing Graphs in the Case Where the Size is Close to the Order
Ryoka Kuriki, Norikazu Takahashi (Okayama Univ.) CAS2017-37 NLP2017-62
Abstract (in Japanese) (See Japanese page) 
(in English) The clustering coefficient is a measure of the tendency of vertices in a network to form clusters. It is known that many networks in the real world have higher clustering coefficients than random networks. However, in order to evaluate how high the clustering coefficient of the network under consideration, we need to find a graph that maximizes the clustering coefficient among all graphs with the same scale as the network, and compare these two values. In this report, we consider the problem of finding a graph that maximizes the global clustering coefficient among all graphs with the given size and order, and give solutions for the case where the size is less than or equal to the order plus four.
Keyword (in Japanese) (See Japanese page) 
(in English) network science / graph theory / global clustering coefficient / maximization / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 226, NLP2017-62, pp. 69-74, Oct. 2017.
Paper # NLP2017-62 
Date of Issue 2017-09-28 (CAS, NLP) 
ISSN Print edition: ISSN 0913-5685    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 CAS2017-37 NLP2017-62

Conference Information
Committee NLP CAS  
Conference Date 2017-10-05 - 2017-10-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Machinaka Campus Nagaoka 
Topics (in Japanese) (See Japanese page) 
Topics (in English) etc. 
Paper Information
Registration To NLP 
Conference Code 2017-10-NLP-CAS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Derivation of Global Clustering Coefficient Maximizing Graphs in the Case Where the Size is Close to the Order 
Sub Title (in English)  
Keyword(1) network science  
Keyword(2) graph theory  
Keyword(3) global clustering coefficient  
Keyword(4) maximization  
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1st Author's Name Ryoka Kuriki  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Norikazu Takahashi  
2nd Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-2 
Date Time 2017-10-06 13:00:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # CAS2017-37, NLP2017-62 
Volume (vol) vol.117 
Number (no) no.225(CAS), no.226(NLP) 
Page pp.69-74 
#Pages 6 
Date of Issue 2017-09-28 (CAS, NLP) 

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