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Paper Abstract and Keywords
Presentation 2021-03-05 14:20
A study of a tunable generative model for graph data using machine learning
Shohei Nakazawa, Yoshiki Sato, Kenji Nakagawa (Nagaoka Univ. of Tech.), Sho Tsugawa (Tsukuba Univ.), Kohei Watabe (Nagaoka Univ. of Tech.) NS2020-159
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, applications and simulations using graphs are becoming more important. The graph has various features, and the simulation results differ depending on the features. Therefore,In recent years, applications and simulations using graphs are becoming more important. A graph has various features, and the simulation results differ depending on the
features. Therefore, a graph generation method for preparing a graph with various features has been studied. Classically, a model that generates using a pre-defined probability distribution of edges and nodes has been studied. In recent years, a method of generating a graph that imitates the learned graph by learning features from actual graph data using machine learning has been studied. However, in conventional research using machine learning, features can be learned from data, but it is not possible to specify features and generate graphs of arbitrary features. In this paper, we propose a model that learns graphs from data and can generate a specified graph by specifying a value of a feature. With the proposed model that learned graphs of various features generated by the conventional method, we verified whether the graphs of arbitrary features could be generated by specifying the features.
Keyword (in Japanese) (See Japanese page) 
(in English) network / graph / generate / conditional VAE / machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 413, NS2020-159, pp. 214-219, March 2021.
Paper # NS2020-159 
Date of Issue 2021-02-25 (NS) 
ISSN Online edition: ISSN 2432-6380
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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 NS  
Conference Date 2021-03-04 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2021-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study of a tunable generative model for graph data using machine learning 
Sub Title (in English)  
Keyword(1) network  
Keyword(2) graph  
Keyword(3) generate  
Keyword(4) conditional VAE  
Keyword(5) machine learning  
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1st Author's Name Shohei Nakazawa  
1st Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
2nd Author's Name Yoshiki Sato  
2nd Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
3rd Author's Name Kenji Nakagawa  
3rd Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
4th Author's Name Sho Tsugawa  
4th Author's Affiliation Tsukuba University (Tsukuba Univ.)
5th Author's Name Kohei Watabe  
5th Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
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Speaker Author-2 
Date Time 2021-03-05 14:20:00 
Presentation Time 20 minutes 
Registration for NS 
Paper # NS2020-159 
Volume (vol) vol.120 
Number (no) no.413 
Page pp.214-219 
#Pages
Date of Issue 2021-02-25 (NS) 


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