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Paper Abstract and Keywords
Presentation 2022-05-12 16:10
Why Do Small Eigenvalues of Laplacian Matrix Improve Anomaly Detection of Temporal Networks?
Eriko Segawa, Yusuke Sakumoto (Kwansei Gakuin Univ.) CQ2022-9
Abstract (in Japanese) (See Japanese page) 
(in English) Many real networks are temporal networks in which the nodes and their relationships change over time, and technology to detect anomalies is needed for such networks. LAD~(Laplacian Anomaly Detection) is an innovative method to detect anomalies in temporal networks using the large eigenvalues of the Laplacian matrix that represents the network structure. In previous work, we clarified that the success rate of anomaly detection by LAD improved significantly using a combination of small and large eigenvalues, but the reason for this has not been fully elucidated. In this paper, we discuss why the combination of large and small eigenvalues is useful for anomaly detection through experiments using temporal networks with various types of anomalies.
Keyword (in Japanese) (See Japanese page) 
(in English) Anormaly Detection / Dynamic Network / Spectral Graph Theory / Laplacian Matrix / Social Network Analysis / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 15, CQ2022-9, pp. 44-49, May 2022.
Paper # CQ2022-9 
Date of Issue 2022-05-05 (CQ) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 CQ2022-9

Conference Information
Committee CQ CS  
Conference Date 2022-05-12 - 2022-05-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukui (Fuku Pref.) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Optical/Wireless Access and Their Integration, Communication Behavior, QoE and Psychology, Assessment / Measurement / Control / Optimization of Communication Quality, Network Services, Wireless Networks, MIMO/Diversity/Multiplexing Techniques, etc. 
Paper Information
Registration To CQ 
Conference Code 2022-05-CQ-CS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Why Do Small Eigenvalues of Laplacian Matrix Improve Anomaly Detection of Temporal Networks? 
Sub Title (in English)  
Keyword(1) Anormaly Detection  
Keyword(2) Dynamic Network  
Keyword(3) Spectral Graph Theory  
Keyword(4) Laplacian Matrix  
Keyword(5) Social Network Analysis  
1st Author's Name Eriko Segawa  
1st Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
2nd Author's Name Yusuke Sakumoto  
2nd Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
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Date Time 2022-05-12 16:10:00 
Presentation Time 25 
Registration for CQ 
Paper # IEICE-CQ2022-9 
Volume (vol) IEICE-122 
Number (no) no.15 
Page pp.44-49 
#Pages IEICE-6 
Date of Issue IEICE-CQ-2022-05-05 

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