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Paper Abstract and Keywords
Presentation 2021-06-22 11:15
A Solution for Recovering Missing Links in Network Topology using Sparse Modeling
Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2021-14 ICSS2021-14
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, sparse modeling, which is a statistical approach, has been applied to many practical problems mostly in the fields of signal processing and image processing, and a dictionary construction method and a sparse representation for network topology with sparse modeling have been proposed in the field of information networking. We believe that a dictionary for network topologies can be utilized for various purposes. In this paper, we investigate how the network topology with missing links can be recovered using a dictionary for network topologies constructed with sparse modeling. Specifically, we propose a method called TRSM (Topology Recovery with Sparse Modeling) that recovers missing links using a dictionary constructed from many teaching network topologies using the overcomplete dictionary construction algorithm called K-SVD (K-means Singular Value Decomposition) algorithm. Furthermore, through experiments, we investigate how accurately the randomly deleted links from a network can be recovered with TRSM. Our finding includes that TRSM can recover missing network topologies more than 50 % regardless of the ratio of deleted links, and the link recovery accuracy of TRSM is higher than the prediction accuracy of existing link prediction methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Link Prediction / l_0-norm Minimization Problem / Network Topology / Overcomplete Dictionary / Sparse Modeling / Sparse Representation / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 68, IA2021-14, pp. 74-79, June 2021.
Paper # IA2021-14 
Date of Issue 2021-06-14 (IA, ICSS) 
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 IA2021-14 ICSS2021-14

Conference Information
Committee IA ICSS  
Conference Date 2021-06-21 - 2021-06-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Security, etc. 
Paper Information
Registration To IA 
Conference Code 2021-06-IA-ICSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Solution for Recovering Missing Links in Network Topology using Sparse Modeling 
Sub Title (in English)  
Keyword(1) Link Prediction  
Keyword(2) l_0-norm Minimization Problem  
Keyword(3) Network Topology  
Keyword(4) Overcomplete Dictionary  
Keyword(5) Sparse Modeling  
Keyword(6) Sparse Representation  
Keyword(7)  
Keyword(8)  
1st Author's Name Ryotaro Matsuo  
1st Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
2nd Author's Name Hiroyuki Ohsaki  
2nd Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
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Speaker Author-1 
Date Time 2021-06-22 11:15:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2021-14, ICSS2021-14 
Volume (vol) vol.121 
Number (no) no.68(IA), no.69(ICSS) 
Page pp.74-79 
#Pages 6 
Date of Issue 2021-06-14 (IA, ICSS) 

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