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Paper Abstract and Keywords
Presentation 2025-01-30 09:40
An Empirical Study on the Applicability of GNN to Graph Reduction
Tomoya Matoba, Ryotaro Matsuo, Ryo Nakamura (Fukuoka Univ.) CQ2024-67
Abstract (in Japanese) (See Japanese page) 
(in English) As information and communication networks continue to grow in size and complexity, handling such large-scale networks has become a significant challenge. One promising approach to address this issue is graph reduction, which reduces the size of graphs. Graph reduction can be broadly classified into two categories: graph sampling and graph coarsening. Both techniques not only provide efficient ways to analyze large-scale networks, but also contribute to speeding up and optimizing procedures (e.g., algorithms) that rely on network structures. From this perspective, research has been conducted to apply the graph reduction to Graph Neural Network (GNN), which is a neural network designed to process data structured as graphs. However, to the best of our knowledge, the interaction between graph reduction and GNNs remains insufficiently explored. In this paper, we focus on node classification, a typical task for GNNs, and empirically analyze the relationship between graph reduction and GNN performance. Specifically, we examine the impact of 12 graph reduction strategies (7 graph sampling strategies and 5 graph coarsening algorithms), the degree of graph reduction, and node classification accuracy. In addition, we investigate the graph features that contribute to node classification by analyzing the correlation between the reduced graph features and classification performance. Our findings show that the impact of the reduction ratio on node classification accuracy depends on the specific graph reduction strategy used.
Keyword (in Japanese) (See Japanese page) 
(in English) Graph Reduction / Graph Sampling / Graph Coarsening / GNN (Graph Neural Network) / Node Classification / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 368, CQ2024-67, pp. 1-6, Jan. 2025.
Paper # CQ2024-67 
Date of Issue 2025-01-23 (CQ) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee CQ CBE  
Conference Date 2025-01-30 - 2025-01-31 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Computational Social Science, Human Computer Interaction, Data Analysis, User Behaviour, Media Quality, etc. 
Paper Information
Registration To CQ 
Conference Code 2025-01-CQ-CBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Empirical Study on the Applicability of GNN to Graph Reduction 
Sub Title (in English)  
Keyword(1) Graph Reduction  
Keyword(2) Graph Sampling  
Keyword(3) Graph Coarsening  
Keyword(4) GNN (Graph Neural Network)  
Keyword(5) Node Classification  
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Keyword(7)  
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1st Author's Name Tomoya Matoba  
1st Author's Affiliation Fukuoka University (Fukuoka Univ.)
2nd Author's Name Ryotaro Matsuo  
2nd Author's Affiliation Fukuoka University (Fukuoka Univ.)
3rd Author's Name Ryo Nakamura  
3rd Author's Affiliation Fukuoka University (Fukuoka Univ.)
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Speaker Author-1 
Date Time 2025-01-30 09:40:00 
Presentation Time 20 minutes 
Registration for CQ 
Paper # CQ2024-67 
Volume (vol) vol.124 
Number (no) no.368 
Page pp.1-6 
#Pages
Date of Issue 2025-01-23 (CQ) 


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