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Paper Abstract and Keywords
Presentation 2026-01-26 12:00
Impact of Network Clustering on Link Removal Methods for Epidemic Suppression
Tatsuya Akiyama, Masaki Komatsu, Yusuke Sakumoto, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) CQ2025-81
Abstract (in Japanese) (See Japanese page) 
(in English) Epidemic processes in which viruses or misinformation propagate through human relationships can be regarded as phenomena occurring on social networks, and their scale and speed are known to depend strongly on the underlying network structure. Epidemic suppression through network interventions such as link removal is expected to be effective; however, excessive interventions may significantly impair social usability. Therefore, there is a strong need for intervention methods that can suppress spreading with minimal intervention while providing clear and explainable rationales for the chosen actions. Previous studies have proposed methods for epidemic suppression by reducing the largest eigenvalue of the adjacency matrix through link removal. However, the effectiveness of such methods is known to depend heavily on network structures, including the spectral gap and clustering proerty. In particular, the strong clustering commonly observed in social networks may violate the assumptions underlying existing methods. In this paper, we focus on existing link removal methods and evaluate how network clustering affects their effectiveness. Through experiments using both synthetic networks and real-world social
networks, we demonstrate that for networks with strong clustering (i.e., small spectral gaps), achieving a target reduction of the largest eigenvalue requires the removal of a large number of links, resulting in a significant degradation of intervention efficiency.
Keyword (in Japanese) (See Japanese page) 
(in English) Epidemic Process / Infection Suppression / Information Diffusion / Social Media / Eigenvalue Manipulation / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 335, CQ2025-81, pp. 39-44, Jan. 2026.
Paper # CQ2025-81 
Date of Issue 2026-01-19 (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 CQ2025-81

Conference Information
Committee CQ CBE  
Conference Date 2026-01-26 - 2026-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) The Beppu Chamber of Commerce and Industry 
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 2026-01-CQ-CBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Impact of Network Clustering on Link Removal Methods for Epidemic Suppression 
Sub Title (in English)  
Keyword(1) Epidemic Process  
Keyword(2) Infection Suppression  
Keyword(3) Information Diffusion  
Keyword(4) Social Media  
Keyword(5) Eigenvalue Manipulation  
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Keyword(7)  
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1st Author's Name Tatsuya Akiyama  
1st Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
2nd Author's Name Masaki Komatsu  
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.)
4th Author's Name Hiroyuki Ohsaki  
4th Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
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Speaker Author-1 
Date Time 2026-01-26 12:00:00 
Presentation Time 20 minutes 
Registration for CQ 
Paper # CQ2025-81 
Volume (vol) vol.125 
Number (no) no.335 
Page pp.39-44 
#Pages 6 
Date of Issue 2026-01-19 (CQ) 

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