Network Resonance Method: Estimating Network Structure from the Resonance of Oscillation Dynamics

Satoshi FURUTANI; Chisa TAKANO; Masaki AIDA

doi:10.1587/transcom.2018EBP3160

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Network Resonance Method: Estimating Network Structure from the Resonance of Oscillation Dynamics

Satoshi FURUTANI, Chisa TAKANO, Masaki AIDA

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Summary :

Spectral graph theory, based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights, provides a useful approach for analyzing network structure. However, in large scale and complex social networks, since it is difficult to completely know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we propose a method for indirectly determining the Laplacian matrix by estimating its eigenvalues and eigenvectors using the resonance of oscillation dynamics on networks.

Publication: IEICE TRANSACTIONS on Communications Vol.E102-B No.4 pp.799-809

Publication Date: 2019/04/01

Publicized: 2018/10/18

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2018EBP3160

Type of Manuscript: PAPER

Category: Fundamental Theories for Communications

Authors

Satoshi FURUTANI
  Tokyo Metropolitan University
Chisa TAKANO
  Hiroshima City University
Masaki AIDA
  Tokyo Metropolitan University

Keyword

Laplacian matrix, spectral graph theory, resonance

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Satoshi FURUTANI, Chisa TAKANO, Masaki AIDA, "Network Resonance Method: Estimating Network Structure from the Resonance of Oscillation Dynamics" in IEICE TRANSACTIONS on Communications, vol. E102-B, no. 4, pp. 799-809, April 2019, doi: 10.1587/transcom.2018EBP3160.
Abstract: Spectral graph theory, based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights, provides a useful approach for analyzing network structure. However, in large scale and complex social networks, since it is difficult to completely know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we propose a method for indirectly determining the Laplacian matrix by estimating its eigenvalues and eigenvectors using the resonance of oscillation dynamics on networks.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3160/_p

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@ARTICLE{e102-b_4_799,
author={Satoshi FURUTANI, Chisa TAKANO, Masaki AIDA, },
journal={IEICE TRANSACTIONS on Communications},
title={Network Resonance Method: Estimating Network Structure from the Resonance of Oscillation Dynamics},
year={2019},
volume={E102-B},
number={4},
pages={799-809},
abstract={Spectral graph theory, based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights, provides a useful approach for analyzing network structure. However, in large scale and complex social networks, since it is difficult to completely know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we propose a method for indirectly determining the Laplacian matrix by estimating its eigenvalues and eigenvectors using the resonance of oscillation dynamics on networks.},
keywords={},
doi={10.1587/transcom.2018EBP3160},
ISSN={1745-1345},
month={April},}

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TY - JOUR
TI - Network Resonance Method: Estimating Network Structure from the Resonance of Oscillation Dynamics
T2 - IEICE TRANSACTIONS on Communications
SP - 799
EP - 809
AU - Satoshi FURUTANI
AU - Chisa TAKANO
AU - Masaki AIDA
PY - 2019
DO - 10.1587/transcom.2018EBP3160
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2019
AB - Spectral graph theory, based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights, provides a useful approach for analyzing network structure. However, in large scale and complex social networks, since it is difficult to completely know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we propose a method for indirectly determining the Laplacian matrix by estimating its eigenvalues and eigenvectors using the resonance of oscillation dynamics on networks.
ER -