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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
조회수
112
네트워크 토폴로지와 링크 가중치를 나타내는 인접 행렬 또는 라플라시안 행렬을 기반으로 하는 스펙트럼 그래프 이론은 네트워크 구조를 분석하는 데 유용한 접근 방식을 제공합니다. 그러나 대규모의 복잡한 소셜 네트워크에서는 네트워크 토폴로지와 링크 가중치를 완전히 아는 것이 어렵기 때문에 이러한 행렬의 구성 요소를 직접 결정할 수 없습니다. 이 문제를 해결하기 위해 우리는 네트워크의 진동 동역학의 공명을 이용하여 고유값과 고유벡터를 추정하여 라플라시안 행렬을 간접적으로 결정하는 방법을 제안합니다.
Satoshi FURUTANI
Tokyo Metropolitan University
Chisa TAKANO
Hiroshima City University
Masaki AIDA
Tokyo Metropolitan University
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부
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
부
@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},}
부
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 -