Reinforcement learning approach for robustness analysis of complex networks with incomplete information

Meng Tian, Zhengcheng Dong and Xianpei Wang

Chaos, Solitons & Fractals, 2021, vol. 144, issue C

Abstract: Network robustness against sequential attacks is significant for complex networks. However, it is generally assumed that complete information of complex networks is obtained and arbitrary nodes can be removed in previous researches. In this paper, a sequential attack in complex networks is modeled as a partial observable Markov decision process (POMDP). Then a reinforcement learning (RL) approach for POMDP is proposed to analyze dynamical robustness of complex networks under sequential attacks, when information of networks is incomplete. According to this approach, an agent can learn to take action by exploiting experiences. To solve the problem of large state space in complex networks, deep Q-network algorithm is used to identify most damaging sequential attacks, as deep neural networks can build up progressively abstract representations of state space of complex networks. The performances of proposed approach are analyzed on scale-free networks and small-world networks. According to the numerical simulations, it is found that the RL-based sequential attacks perform better when load distributions are more heterogeneous and local connections are more significant. Furthermore, it is shown that increasing the proportions of observed and attacked nodes improves the performance of RL-based sequential attacks. Finally, the results are verified on the IEEE 300-bus system and the simulation results highlight the damages caused by RL-based sequential attacks.

Keywords: Robustness; Complex networks; Sequential attacks; Incomplete information; Reinforcement learning; Deep learning (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077920310341
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077920310341

DOI: 10.1016/j.chaos.2020.110643

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().