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Paper Abstract and Keywords
Presentation 2022-12-23 13:00
Initial evaluation of node embedding using quantum walk
Rei Sato, Shuichiro Haruta, Kazuhiro Saito, Mori Kurokawa (KDDI Research, Inc.) IBISML2022-57
Abstract (in Japanese) (See Japanese page) 
(in English) DeepWalk is one of the node-embedding methods which represents node features using sequences obtained from a random walk on graphs. In this study, we use a quantum walk, which is computationally faster than random walks, to evaluate the accuracy of node embedding for the node classification task.
Specifically, we propose to use the probability of the existence of a quantum walker at a node at each time (time-averaged probability) as a feature of node classification. Considering that the quantum walk is affected by the initial state of the quantum walker, we use two types of features: one is a quantum walk from all nodes, and the other is a quantum walk from each node. We use three medium sized real-data graphs as validation data, and compare the accuracy of the node classification tasks obtained by DeepWalk and by quantum walk. For some graphs, we show that quantum walk are superior to DeepWalk in node features.
Keyword (in Japanese) (See Japanese page) 
(in English) Graph embedding / DeepWalk / Quantum walk / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 325, IBISML2022-57, pp. 101-105, Dec. 2022.
Paper # IBISML2022-57 
Date of Issue 2022-12-15 (IBISML) 
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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Conference Information
Committee IBISML  
Conference Date 2022-12-22 - 2022-12-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2022-12-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Initial evaluation of node embedding using quantum walk 
Sub Title (in English)  
Keyword(1) Graph embedding  
Keyword(2) DeepWalk  
Keyword(3) Quantum walk  
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1st Author's Name Rei Sato  
1st Author's Affiliation KDDI Research, Inc. (KDDI Research, Inc.)
2nd Author's Name Shuichiro Haruta  
2nd Author's Affiliation KDDI Research, Inc. (KDDI Research, Inc.)
3rd Author's Name Kazuhiro Saito  
3rd Author's Affiliation KDDI Research, Inc. (KDDI Research, Inc.)
4th Author's Name Mori Kurokawa  
4th Author's Affiliation KDDI Research, Inc. (KDDI Research, Inc.)
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Speaker Author-1 
Date Time 2022-12-23 13:00:00 
Presentation Time 20 minutes 
Registration for IBISML 
Paper # IBISML2022-57 
Volume (vol) vol.122 
Number (no) no.325 
Page pp.101-105 
#Pages 5 
Date of Issue 2022-12-15 (IBISML) 

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