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Paper Abstract and Keywords
Presentation 2026-07-23 09:00
[Poster Presentation] Study on the Effects of Graph Spectrum Shift on the Training Convergence Speed of GNNs
Sonu Ando, Koki Kampu, Yusuke Sakumoto (Kwansei Gakuin Univ.) CQ2026-41
Abstract (in Japanese) (See Japanese page) 
(in English) Many real-world graph datasets are dynamic graphs, in which the graph structure and the properties of individual nodes change over time. To apply GNNs to such dynamic graphs, it is important to update the model in response to changes in the graph and to achieve sufficient classification accuracy within a short training time. Feature propagation in GNNs is closely related to the eigenvalue distribution of the graph structural information matrix, and this eigenvalue distribution may affect how features propagate over the graph and how much information is aggregated from neighboring nodes. Therefore, in this study, we use Graph Spectrum Shift (GSS), an operation that shifts the eigenvalue distribution of a matrix while preserving its eigenvectors, and evaluate how changes in the eigenvalue distribution of the graph structural information matrix affect the training speed of GNNs. In this evaluation, we compare the effect of GSS on the training speed using various datasets and GNN models under the same evaluation criterion.
Keyword (in Japanese) (See Japanese page) 
(in English) Graph Neural Network / Semi-Supervised Node Classification / Learning Speed / Spectral Graph Theory / Eigenvalue Distribution / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 121, CQ2026-41, pp. 148-153, July 2026.
Paper # CQ2026-41 
Date of Issue 2026-07-14 (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 CQ2026-41

Conference Information
Committee CQ  
Conference Date 2026-07-21 - 2026-07-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Eef Jouhou Plaza 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CQ 
Conference Code 2026-07-CQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on the Effects of Graph Spectrum Shift on the Training Convergence Speed of GNNs 
Sub Title (in English)  
Keyword(1) Graph Neural Network  
Keyword(2) Semi-Supervised Node Classification  
Keyword(3) Learning Speed  
Keyword(4) Spectral Graph Theory  
Keyword(5) Eigenvalue Distribution  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Sonu Ando  
1st Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
2nd Author's Name Koki Kampu  
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 2026-07-23 09:00:00 
Presentation Time 60 minutes 
Registration for CQ 
Paper # CQ2026-41 
Volume (vol) vol.126 
Number (no) no.121 
Page pp.148-153 
#Pages 6 
Date of Issue 2026-07-14 (CQ) 

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