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Paper Abstract and Keywords
Presentation 2021-09-09 10:55
Proposal of an Improving Method for the Laplacian Anomaly Detection of Temporal Networks
Eriko Segawa, Toyoaki Taniguchi, Yusuke Sakumoto (Kwansei Gakuin Univ.) CQ2021-40
Abstract (in Japanese) (See Japanese page) 
(in English) Many networks in the real world are dynamic and temporal wherein relationships among nodes change with time. Technologies to detect anomalies in dynamic networks are crucial to investigate various problems of society. The Laplacian anomaly detection (LAD) has been proposed as an innovative method for detecting anomalies in dynamic networks. LAD calculates the anomaly score, i.e., an indicator of the degree of deviation from the normal state, at each time from timeseries data for some eigenvalues of the Laplacian matrix, which represents the structure of networks. Using the calculated anomaly scores, LAD detects the anomaly in the dynamic network. The original LAD uses only the large eigenvalues of the Laplacian matrix; other eigenvalue combinations have not been discussed for anomaly detection. Based on spectral graph theory, small eigenvalues contain relevant information about the global structure of a network. Therefore, small eigenvalues are crucial for detecting large-scale anomalies in dynamic networks, and their use should improve the accuracy of LAD. Herein, we propose an improvement that uses not only the large eigenvalues but also the small eigenvalues of the Laplacian matrix for calculating anomaly scores in LAD. Through the evaluation, we clarify that the proposed improvement can significantly improve the accuracy of anomaly detection in LAD.
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. 121, no. 173, CQ2021-40, pp. 17-22, Sept. 2021.
Paper # CQ2021-40 
Date of Issue 2021-09-02 (CQ) 
ISSN 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 CQ2021-40

Conference Information
Committee CQ MIKA  
Conference Date 2021-09-09 - 2021-09-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Wireless Communications Quality, 6G, IoT, Resource Management, Wireless Transmission, Cross layer Technologies, etc. 
Paper Information
Registration To CQ 
Conference Code 2021-09-CQ-MIKA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Proposal of an Improving Method for the Laplacian 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 Toyoaki Taniguchi  
2nd Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
3rd Author's Name Yusuke Sakumoto  
3rd Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
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Speaker Author-1 
Date Time 2021-09-09 10:55:00 
Presentation Time 20 minutes 
Registration for CQ 
Paper # CQ2021-40 
Volume (vol) vol.121 
Number (no) no.173 
Page pp.17-22 
Date of Issue 2021-09-02 (CQ) 

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