The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
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A teoria dos grafos espectrais, baseada na matriz de adjacência ou na matriz Laplaciana que representa a topologia da rede e os pesos dos links, fornece uma abordagem útil para analisar a estrutura da rede. Porém, em redes sociais complexas e de grande escala, como é difícil conhecer completamente a topologia da rede e os pesos dos links, não podemos determinar diretamente os componentes dessas matrizes. Para resolver este problema, propomos um método para determinar indiretamente a matriz Laplaciana, estimando seus autovalores e autovetores usando a ressonância da dinâmica de oscilação em redes.
Satoshi FURUTANI
Tokyo Metropolitan University
Chisa TAKANO
Hiroshima City University
Masaki AIDA
Tokyo Metropolitan University
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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 -