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Paper Abstract and Keywords
Presentation 2025-12-11 11:35
Mulit-Step Weighted Random Walk for Local Community Detection
Shun Totsuka, Tsuyoshi Yamashita, Kunitake Kaneko (Keio Univ.) IN2025-43
Abstract (in Japanese) (See Japanese page) 
(in English) In the analysis of large-scale networks, random walk (RW)-based approaches that identify communities locally from the reachability distribution of a seed node are widely used. However, in conventional RWs, transitions are made uniformly to all neighboring nodes. As a result, when the seed lies near the boundary of multiple communities, the walk disperses, leading to the incorrect detection of nodes from different communities as belonging to the same community.
To address this problem, the proposed method introduces directionality into RW transitions by leveraging the observation that a seed node’s neighbors tend to form groups corresponding to their underlying communities. Specifically, in each step of the RW—particularly the first step—transitions to neighboring nodes are weighted based on two structural characteristics of connectivity-based partitions of the neighbor set: their sizes and internal edge densities. Furthermore, by tuning the parameters that control this weighting, the method is designed to extract different communities depending on the parameter values.
Experimental evaluations on real-world datasets demonstrate that the proposed method identifies communities with lower conductance and longer average path lengths from the seed node to nodes within the community compared with existing methods. The results also show that varying the parameter values yields communities exhibiting different conductance and average path length characteristics.
Keyword (in Japanese) (See Japanese page) 
(in English) Information Network / Random Walk / Community Detection / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 285, IN2025-43, pp. 40-47, Dec. 2025.
Paper # IN2025-43 
Date of Issue 2025-12-04 (IN) 
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 IN2025-43

Conference Information
Committee IN IA  
Conference Date 2025-12-11 - 2025-12-12 
Place (in Japanese) (See Japanese page) 
Place (in English) The Kanazawa Chamber of Commerce & Industry 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Performance Analysis and Simulation, Robustness, Traffic and Throughput Measurement, Quality of Service (QoS) Control, Congestion Control, Overlay Network/P2P, CCN, ICN, Multicast, Routing, Security, etc. 
Paper Information
Registration To IN 
Conference Code 2025-12-IN-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Mulit-Step Weighted Random Walk for Local Community Detection 
Sub Title (in English)  
Keyword(1) Information Network  
Keyword(2) Random Walk  
Keyword(3) Community Detection  
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1st Author's Name Shun Totsuka  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Tsuyoshi Yamashita  
2nd Author's Affiliation Research Institute for Digital Media and Content, Keio University. (Keio Univ.)
3rd Author's Name Kunitake Kaneko  
3rd Author's Affiliation Keio University/Research Institute for Digital Media and Content, Keio University. (Keio Univ.)
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Speaker Author-1 
Date Time 2025-12-11 11:35:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2025-43 
Volume (vol) vol.125 
Number (no) no.285 
Page pp.40-47 
#Pages 8 
Date of Issue 2025-12-04 (IN) 

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