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
복잡한 네트워크의 견고성은 잠재적인 위협으로 인해 정점이나 링크가 제거되는 경우 성능을 향상시키기 위한 필수 주제입니다. 최근 몇 년 동안 많은 연구자들이 이 분야에서 상당한 발전을 이루었습니다. 이 논문에서는 새로운 통계적 관점에서 개요를 보여줍니다. 먼저 2개의 기본 네트워크 모델, 12개의 널리 사용되는 공격 전략 및 가장 설득력 있는 네트워크 견고성 지표를 포함하여 복잡한 네트워크에 대한 간략한 검토를 제시합니다. 그런 다음 12가지 공격 전략의 상호 상관 관계와 네트워크 모델 간 상관 관계의 차이에 중점을 둡니다. 또한 다양한 공격 전략에 따라 정점이 제거될 때 네트워크의 견고성과 네트워크 모델 간 견고성의 차이에 대해서도 궁금합니다. 우리의 목표는 서로 다른 네트워크 모델에 대한 중심성의 상관 메커니즘을 관찰하고, 서로 다른 중심성이 서로 다른 네트워크 모델에 대한 공격 디렉터로 적용될 때 네트워크 견고성을 비교하는 것입니다. 우리에게 영감을 주는 것은 어쩌면 여러 가지 고파괴 공격 전략을 결합하여 딥 러닝 프레임워크를 기반으로 최적의 전략을 찾는 패러다임을 찾을 수 있다는 것입니다.
Xin-Ling GUO
Zhejiang University
Zhe-Ming LU
Zhejiang University
Yi-Jia ZHANG
Zhejiang Sci-Tech University
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.
부
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
부
@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},}
부
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 -