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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 |
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| 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 | 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 | CQ2022-9 |
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| 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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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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| Speaker | Author-1 |
| Date Time | 2022-05-12 16:10:00 |
| Presentation Time | 25 minutes |
| Registration for | CQ |
| Paper # | CQ2022-9 |
| Volume (vol) | vol.122 |
| Number (no) | no.15 |
| Page | pp.44-49 |
| #Pages | 6 |
| Date of Issue | 2022-05-05 (CQ) |
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