IEICE TRANSACTIONS on Information
Correlation of Centralities: A Study through Distinct Graph Robustness
Summary :
Robustness of complex networks is an essential subject for improving their performance when vertices or links are removed due to potential threats. In recent years, significant advancements have been achieved in this field by many researchers. In this paper we show an overview from a novel statistic perspective. We present a brief review about complex networks at first including 2 primary network models, 12 popular attack strategies and the most convincing network robustness metrics. Then, we focus on the correlations of 12 attack strategies with each other, and the difference of the correlations from one network model to the other. We are also curious about the robustness of networks when vertices are removed according to different attack strategies and the difference of robustness from one network model to the other. Our aim is to observe the correlation mechanism of centralities for distinct network models, and compare the network robustness when different centralities are applied as attacking directors to distinct network models. What inspires us is that maybe we can find a paradigm that combines several high-destructive attack strategies to find the optimal strategy based on the deep learning framework.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.7 pp.1054-1057
- Publication Date
- 2021/07/01
- Publicized
- 2021/04/05
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDL8163
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Xin-Ling GUO
Zhejiang University
Zhe-Ming LU
Zhejiang University
Yi-Jia ZHANG
Zhejiang Sci-Tech University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Xin-Ling GUO, Zhe-Ming LU, Yi-Jia ZHANG, "Correlation of Centralities: A Study through Distinct Graph Robustness" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 7, pp. 1054-1057, July 2021, doi: 10.1587/transinf.2020EDL8163.
Abstract: Robustness of complex networks is an essential subject for improving their performance when vertices or links are removed due to potential threats. In recent years, significant advancements have been achieved in this field by many researchers. In this paper we show an overview from a novel statistic perspective. We present a brief review about complex networks at first including 2 primary network models, 12 popular attack strategies and the most convincing network robustness metrics. Then, we focus on the correlations of 12 attack strategies with each other, and the difference of the correlations from one network model to the other. We are also curious about the robustness of networks when vertices are removed according to different attack strategies and the difference of robustness from one network model to the other. Our aim is to observe the correlation mechanism of centralities for distinct network models, and compare the network robustness when different centralities are applied as attacking directors to distinct network models. What inspires us is that maybe we can find a paradigm that combines several high-destructive attack strategies to find the optimal strategy based on the deep learning framework.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8163/_p
Copy
@ARTICLE{e104-d_7_1054,
author={Xin-Ling GUO, Zhe-Ming LU, Yi-Jia ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Correlation of Centralities: A Study through Distinct Graph Robustness},
year={2021},
volume={E104-D},
number={7},
pages={1054-1057},
abstract={Robustness of complex networks is an essential subject for improving their performance when vertices or links are removed due to potential threats. In recent years, significant advancements have been achieved in this field by many researchers. In this paper we show an overview from a novel statistic perspective. We present a brief review about complex networks at first including 2 primary network models, 12 popular attack strategies and the most convincing network robustness metrics. Then, we focus on the correlations of 12 attack strategies with each other, and the difference of the correlations from one network model to the other. We are also curious about the robustness of networks when vertices are removed according to different attack strategies and the difference of robustness from one network model to the other. Our aim is to observe the correlation mechanism of centralities for distinct network models, and compare the network robustness when different centralities are applied as attacking directors to distinct network models. What inspires us is that maybe we can find a paradigm that combines several high-destructive attack strategies to find the optimal strategy based on the deep learning framework.},
keywords={},
doi={10.1587/transinf.2020EDL8163},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Correlation of Centralities: A Study through Distinct Graph Robustness
T2 - IEICE TRANSACTIONS on Information
SP - 1054
EP - 1057
AU - Xin-Ling GUO
AU - Zhe-Ming LU
AU - Yi-Jia ZHANG
PY - 2021
DO - 10.1587/transinf.2020EDL8163
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2021
AB - Robustness of complex networks is an essential subject for improving their performance when vertices or links are removed due to potential threats. In recent years, significant advancements have been achieved in this field by many researchers. In this paper we show an overview from a novel statistic perspective. We present a brief review about complex networks at first including 2 primary network models, 12 popular attack strategies and the most convincing network robustness metrics. Then, we focus on the correlations of 12 attack strategies with each other, and the difference of the correlations from one network model to the other. We are also curious about the robustness of networks when vertices are removed according to different attack strategies and the difference of robustness from one network model to the other. Our aim is to observe the correlation mechanism of centralities for distinct network models, and compare the network robustness when different centralities are applied as attacking directors to distinct network models. What inspires us is that maybe we can find a paradigm that combines several high-destructive attack strategies to find the optimal strategy based on the deep learning framework.
ER -
English
