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 |
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) |
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NS2020-159 |
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) |
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Keyword(1) |
network |
Keyword(2) |
graph |
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generate |
Keyword(4) |
conditional VAE |
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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 |
6 |
Date of Issue |
2021-02-25 (NS) |
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