AUTHOR=Cai Meng , Liu Jiaqi , Cui Ying 

TITLE=Network Robustness Analysis Based on Maximum Flow

JOURNAL=Frontiers in Physics

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.792410

DOI=10.3389/fphy.2021.792410

ISSN=2296-424X

ABSTRACT=Network robustness is the ability of a network to maintain a certain level of structural integrity and its original functions after being attacked, and it is the key to whether the damaged network can continue to operate normally. We define two types of robustness evaluation indicators based on network maximum flow: the flow capacity robustness, which assesses the ability of the network to resist attack, and the flow recovery robustness, which assesses the ability to rebuild the network after an attack on the network. In order to verify effectiveness of the robustness metrics proposed in this paper, we simulate four typical networks and analyze their robustness, the results show that: high-density ER random network is stronger than low-density network in terms of connectivity and resilience; the growth rate parameter of BA scale-free network does not have a significant impact on robustness changes in most cases; the greater the average degree of regular network, the greater the robustness; robustness of WS small-world networks increases with the increase of reconnection probability and average degree. In addition, there is a critical damage rate (when node damage rate is less than this critical value, the damaged nodes and edges can almost be completely recovered) when examining the flow recovery robustness, and the critical damage rate is located near 20%. The flow capacity robustness and the flow recovery enrich the network structure indicator system and more comprehensively describe structural stability of real networks.