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Paper Abstract and Keywords
Presentation 2021-01-18 10:30
Local community and weak ties detection using random walk on hypergraph.
Ryo Oka (Keio Univ.), Yuuki Takai (RIKEN), Naoki Matsumoto (Keio Univ.), Masahiro Ikeda (RIKEN), Kunitake Kaneko (Keio Univ.) IN2020-41
Abstract (in Japanese) (See Japanese page) 
(in English) There are many services that use a graph with a content as a vertex and their relationship as a edge. Community detection or graph clustering is one of the fundamental problems in graph characterization. The word "community" here is defined as a relatively dense set of vertices in a graph. Many existing community detection algorithms often assume the analysis of the global graph, but it is difficult to analyze the global graph as they would like. Also, many existing methods do not assume hypergraphs. We propose an algorithm for extracting the local community that include the starting vertex and the weak ties connected to it, while limiting range of the analysis of the graph to the surrounding of the starting vertex by random walk based on Personalized PageRank. We then reveal the relationship between the number of random walk trials and their accuracy using existing metrics and a synthetic network with ground-truth community. As a result, using hypergraphs which has 1000 vertices created by Stochastic Block Model, we found that the $F_1$ value of the community can remain above about 0.8 for $mu =0.1-0.5$ during a random walk of 2000 steps.
Keyword (in Japanese) (See Japanese page) 
(in English) community detection / weak ties / random walk / hypergraph / personalized pagerank / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 311, IN2020-41, pp. 1-6, Jan. 2021.
Paper # IN2020-41 
Date of Issue 2021-01-11 (IN) 
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 IN  
Conference Date 2021-01-18 - 2021-01-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Contents Distribution, Social Networking Services, Data Analytics and Processing Platform, Big data, etc. 
Paper Information
Registration To IN 
Conference Code 2021-01-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Local community and weak ties detection using random walk on hypergraph. 
Sub Title (in English)  
Keyword(1) community detection  
Keyword(2) weak ties  
Keyword(3) random walk  
Keyword(4) hypergraph  
Keyword(5) personalized pagerank  
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1st Author's Name Ryo Oka  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Yuuki Takai  
2nd Author's Affiliation RIKEN Center for Advanced Intelligence Project (RIKEN)
3rd Author's Name Naoki Matsumoto  
3rd Author's Affiliation Keio University (Keio Univ.)
4th Author's Name Masahiro Ikeda  
4th Author's Affiliation RIKEN Center for Advanced Intelligence Project (RIKEN)
5th Author's Name Kunitake Kaneko  
5th Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2021-01-18 10:30:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2020-41 
Volume (vol) vol.120 
Number (no) no.311 
Page pp.1-6 
#Pages
Date of Issue 2021-01-11 (IN) 


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