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Paper Abstract and Keywords
Presentation 2017-03-03 11:00
Reconstruction of Network Structure from Incomplete Set of Observed Information by Using Compressed Sensing
Shun Sugimoto, Masaki Aida (TMU) IN2016-143
Abstract (in Japanese) (See Japanese page) 
(in English) On complex large scale networks like social networks, it is typically impossible to observe complete information about their topology structure or link weight directly. Recent research has proposed the network resonance method that enables to estimate the eigenvalues of the Laplacian matrix representing network structure, by using resonance phenomena of oscillation dynamics on network. In addition, we are developing the estimation method of eigenvectors of the Laplacian matrix based on the resonance. Eigenvalues and eigenvectors estimated in this method have errors, and realistically, it is not always able to be observed all of them. In this paper, by applying the compressed
sensing, we propose the new method reconstructing the original Laplacian matrix from a part of its eigenvalues and eigenvectors. If we can reconstruct Laplacian matrix, we expect to understand topology structure or link weight of social networks.
Keyword (in Japanese) (See Japanese page) 
(in English) compressed sensing / Laplacian matrix / real symmetric matrix / eigenvalue / eigenvector / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 485, IN2016-143, pp. 275-280, March 2017.
Paper # IN2016-143 
Date of Issue 2017-02-23 (IN) 
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 IN2016-143

Conference Information
Committee NS IN  
Conference Date 2017-03-02 - 2017-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWA ZANPAMISAKI ROYAL HOTEL 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To IN 
Conference Code 2017-03-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reconstruction of Network Structure from Incomplete Set of Observed Information by Using Compressed Sensing 
Sub Title (in English)  
Keyword(1) compressed sensing  
Keyword(2) Laplacian matrix  
Keyword(3) real symmetric matrix  
Keyword(4) eigenvalue  
Keyword(5) eigenvector  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Shun Sugimoto  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Masaki Aida  
2nd Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2017-03-03 11:00:00 
Presentation Time 20 minutes 
Registration for IN 
Paper # IN2016-143 
Volume (vol) vol.116 
Number (no) no.485 
Page pp.275-280 
#Pages 6 
Date of Issue 2017-02-23 (IN) 

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