| IEICE Technical Committee Submission System Conference Paper's Information |
Online Proceedings [Sign in] Tech. Rep. Archives |
| Go Top Page | Go Previous | [Japanese] / [English] |
| Paper Abstract and Keywords | ||
| Presentation | 2021-07-14 10:55 Relaxation of Network Restriction for Deep Learning Based Consensus Problem with Eigenvector Centrality Shoya Ogawa, Koji Ishii (Kagawa Univ.)  RCC2021-23 |
|
| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | he convergence performance of consensus problems depends on the applied weighting factors into individual edges. Unfortunately, in the case with a complex network, the computation of optimum weighting factors is unfeasible due to high complexity. Kishida et.al., have recently proposed to apply a deep learning technique to the computation of weighting factors and shown that the deep leaning aided consensus problem can significantly enhance the convergence performance. However, since Kishida's method provides the optimum weighting factors only for the focused network topology, the calculated weighting factors cannot apply to the case with different network topology. To relax this restriction, this study proposes a learning method in which the weighting factors are computed with the constraint caused by the centrality of network. We first embed the constraint of eigenvector-centrality into the learning procedure, and the training is done with the training data which is generated with different network topology but with the same stochastic property. By doing so, the proposed method can lean the stochastic property of the network. Simulation results show that the proposed method with eigenvector centrality cannot achieve better performance than the one with degree centrality. But, both optimization can achieve better performance than the case with fixed value. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Consensus Problem / data-driven algorithm / deep-unfolding / eigenvector centrality / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 101, RCC2021-23, pp. 7-12, July 2021. | |
| Paper # | RCC2021-23 | |
| Date of Issue | 2021-07-07 (RCC) | |
| 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 | RCC2021-23 |
|
| Conference Information | |
| Committee | RCS SR NS SeMI RCC |
| Conference Date | 2021-07-14 - 2021-07-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication and Network Technology of the AI Age, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Paper Information | |
| Registration To | RCC |
| Conference Code | 2021-07-RCS-SR-NS-SeMI-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Relaxation of Network Restriction for Deep Learning Based Consensus Problem with Eigenvector Centrality |
| Sub Title (in English) | |
| Keyword(1) | Consensus Problem |
| Keyword(2) | data-driven algorithm |
| Keyword(3) | deep-unfolding |
| Keyword(4) | eigenvector centrality |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shoya Ogawa |
| 1st Author's Affiliation | Kagawa University (Kagawa Univ.) |
| 2nd Author's Name | Koji Ishii |
| 2nd Author's Affiliation | Kagawa University (Kagawa Univ.) |
| 3rd Author's Name | |
| 3rd Author's Affiliation | () |
| 4th Author's Name | |
| 4th Author's Affiliation | () |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| 7th Author's Name | |
| 7th Author's Affiliation | () |
| 8th Author's Name | |
| 8th Author's Affiliation | () |
| 9th Author's Name | |
| 9th Author's Affiliation | () |
| 10th Author's Name | |
| 10th Author's Affiliation | () |
| 11th Author's Name | |
| 11th Author's Affiliation | () |
| 12th Author's Name | |
| 12th Author's Affiliation | () |
| 13th Author's Name | |
| 13th Author's Affiliation | () |
| 14th Author's Name | |
| 14th Author's Affiliation | () |
| 15th Author's Name | |
| 15th Author's Affiliation | () |
| 16th Author's Name | |
| 16th Author's Affiliation | () |
| 17th Author's Name | |
| 17th Author's Affiliation | () |
| 18th Author's Name | |
| 18th Author's Affiliation | () |
| Speaker | Author-1 |
| Date Time | 2021-07-14 10:55:00 |
| Presentation Time | 25 minutes |
| Registration for | RCC |
| Paper # | RCC2021-23 |
| Volume (vol) | vol.121 |
| Number (no) | no.101 |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2021-07-07 (RCC) |
[Return to Top Page]