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Paper Abstract and Keywords
Presentation 2017-12-15 12:40
Information Propagation Analysis of Social Network Using the Universality of Random Matrix
Tsukasa Kameyama, Yusuke Sakumoto (Tokyo Metropolitan Univ.), Chisa Takano (Hiroshima City Univ.), Masaki Aida (Tokyo Metropolitan Univ.) IA2017-62
Abstract (in Japanese) (See Japanese page) 
(in English) Spectral graph theory gives an algebraical approach to analyze network structure by using a matrix. However, it is difficult for large-scale social networks to know the details of its network structure including link weights, and we cannot expect that a matrix representing the network structure is given as a priori knowledge. In this paper, we analyze the information propagation characteristics using the universality of random matrices that represents the structure of a large-scale social network. An existing study has been reported that the spectral density distribution of the normalized Laplacian matrix of networks with weightless links follows the Wigner semicircle law under a certain condition. In this paper, we evaluate the spectral density distribution of the normalized Laplacian matrix for randomly weighted links, and investigate conditions for the spectral density distribution to satisfy the Wigner semicircle law, experimentally. In addition, we use the Wigner semicircle law for the social network analysis, and clarify an universal property of information propagation on large-scale social networks.
Keyword (in Japanese) (See Japanese page) 
(in English) social network / normalized Laplacian matrix / random matrix / spectral density distribution / Wigner semicircle law / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 354, IA2017-62, pp. 55-60, Dec. 2017.
Paper # IA2017-62 
Date of Issue 2017-12-07 (IA) 
ISSN Print edition: ISSN 0913-5685    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 IA IN  
Conference Date 2017-12-14 - 2017-12-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima City Univ. 
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, IPv6, Multicast, Routing, DDoS, etc. 
Paper Information
Registration To IA 
Conference Code 2017-12-IA-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Information Propagation Analysis of Social Network Using the Universality of Random Matrix 
Sub Title (in English)  
Keyword(1) social network  
Keyword(2) normalized Laplacian matrix  
Keyword(3) random matrix  
Keyword(4) spectral density distribution  
Keyword(5) Wigner semicircle law  
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1st Author's Name Tsukasa Kameyama  
1st Author's Affiliation Tokyo Metropolitan University (Tokyo Metropolitan Univ.)
2nd Author's Name Yusuke Sakumoto  
2nd Author's Affiliation Tokyo Metropolitan University (Tokyo Metropolitan Univ.)
3rd Author's Name Chisa Takano  
3rd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
4th Author's Name Masaki Aida  
4th Author's Affiliation Tokyo Metropolitan University (Tokyo Metropolitan Univ.)
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Speaker Author-1 
Date Time 2017-12-15 12:40:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2017-62 
Volume (vol) vol.117 
Number (no) no.354 
Page pp.55-60 
#Pages 6 
Date of Issue 2017-12-07 (IA) 

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