Method for efficiently orthogonalizing the eigenvectors of the Laplacian matrix to estimate social network structure-Reference-Cited by-同舟云学术

Method for efficiently orthogonalizing the eigenvectors of the Laplacian matrix to estimate social network structure

Published:2020 Issue:1 Volume:11 Page:60-67
ISSN:2185-4106
Container-title:Nonlinear Theory and Its Applications, IEICE
language:en
Short-container-title:NOLTA

Author:

Hirakura Naoki¹,Takano Chisa²^ORCID,Aida Masaki¹^ORCID

Affiliation:

1. Graduate School of Systems Design, Tokyo Metropolitan University

2. Graduate School of Information Sciences, Hiroshima City University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Rehabilitation,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine

Link

https://www.jstage.jst.go.jp/article/nolta/11/1/11_60/_pdf

Reference11 articles.

1. [1] M. Aida, C. Takano, and M. Murata, “Oscillation model for network dynamics caused by asymmetric node interaction based on the symmetric scaled Laplacian matrix,” The 12th International Conference on Foundations of Computer Science (FCS 2016), pp. 38-44, 2016.

2. [2] M. Aida, C. Takano, and M. Murata, “Oscillation model for describing network dynamics caused by asymmetric node interaction,” IEICE Transactions on Communications, vol. 101-B, no. 1, pp. 123-136, 2018.

3. [3] S. Furutani, C. Takano, and M. Aida, “Network resonance method: Estimating network structure from the resonance of oscillation dynamics,” IEICE Transactions on Communications, vol. 102-B, no. 4, pp. 799-809, 2019.

4. [4] S. Furutani, C. Takano, and M. Aida, “Method for estimating the eigenvectors of a scaled Laplacian matrix using the resonance of oscillation dynamics on networks,” Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 615-618, ACM, 2017.

5. [5] F.R. Chung and F.C. Graham, “Spectral graph theory,” American Mathematical Soc., 1997.