Literature DB >> 23005185

Enhancing network robustness against malicious attacks.

An Zeng1, Weiping Liu.   

Abstract

In a recent work [Schneider et al., Proc. Natl. Acad. Sci. USA 108, 3838 (2011)], the authors proposed a simple measure for network robustness under malicious attacks on nodes. Using a greedy algorithm, they found that the optimal structure with respect to this quantity is an onion structure in which high-degree nodes form a core surrounded by rings of nodes with decreasing degree. However, in real networks the failure can also occur in links such as dysfunctional power cables and blocked airlines. Accordingly, complementary to the node-robustness measurement (R(n)), we propose a link-robustness index (R(l)). We show that solely enhancing R(n) cannot guarantee the improvement of R(l). Moreover, the structure of an R(l)-optimized network is found to be entirely different from that of an onion network. In order to design robust networks that are resistant to a more realistic attack condition, we propose a hybrid greedy algorithm that takes both the R(n) and R(l) into account. We validate the robustness of our generated networks against malicious attacks mixed with both nodes and links failure. Finally, some economical constraints for swapping the links in real networks are considered, and significant improvement in both aspects of robustness is still achieved.

Entities:  

Mesh:

Year:  2012        PMID: 23005185     DOI: 10.1103/PhysRevE.85.066130

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  9 in total

1.  Predicting the evolution of spreading on complex networks.

Authors:  Duan-Bing Chen; Rui Xiao; An Zeng
Journal:  Sci Rep       Date:  2014-08-18       Impact factor: 4.379

2.  Hardness Analysis and Empirical Studies of the Relations among Robustness, Topology and Flow in Dynamic Networks.

Authors:  Xing Zhou; Wei Peng; Zhen Xu; Bo Yang
Journal:  PLoS One       Date:  2015-12-22       Impact factor: 3.240

3.  The robustness of ecosystems to the species loss of community.

Authors:  Qing Cai; Jiming Liu
Journal:  Sci Rep       Date:  2016-10-27       Impact factor: 4.379

4.  Trade-offs between robustness and small-world effect in complex networks.

Authors:  Guan-Sheng Peng; Suo-Yi Tan; Jun Wu; Petter Holme
Journal:  Sci Rep       Date:  2016-11-17       Impact factor: 4.379

5.  Network Anatomy Controlling Abrupt-like Percolation Transition.

Authors:  Hirokazu Kawamoto; Hideki Takayasu; Misako Takayasu
Journal:  Sci Rep       Date:  2017-03-13       Impact factor: 4.379

6.  Maximizing Network Resilience against Malicious Attacks.

Authors:  Wenguo Li; Yong Li; Yi Tan; Yijia Cao; Chun Chen; Ye Cai; Kwang Y Lee; Michael Pecht
Journal:  Sci Rep       Date:  2019-02-19       Impact factor: 4.379

7.  Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks.

Authors:  Yuan Feng; Baoan Ren; Chengyi Zeng; Yuyuan Yang; Hongfu Liu
Journal:  Entropy (Basel)       Date:  2020-07-29       Impact factor: 2.524

8.  Mandala networks: ultra-small-world and highly sparse graphs.

Authors:  Cesar I N Sampaio Filho; André A Moreira; Roberto F S Andrade; Hans J Herrmann; José S Andrade
Journal:  Sci Rep       Date:  2015-03-13       Impact factor: 4.379

9.  A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural Failures.

Authors:  Delin Huang; Xiaojun Tan; Nanjie Chen; Zhengping Fan
Journal:  Sensors (Basel)       Date:  2022-03-11       Impact factor: 3.576

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.