Network resilience of non-hub nodes failure under memory and non-memory based attacks with limited information

Abstract

Previous studies on network robustness mainly concentrated on hub node failures with fully known network structure information. However, hub nodes are often well protected and not accessible to damage or malfunction in a real-world networked system. In addition, one can only gain insight into limited network connectivity knowledge due to large-scale properties and dynamic changes of the network itself. In particular, two different aggression patterns are present in a network attack: memory based attack, in which failed nodes are not attacked again, or non-memory based attack; that is, nodes can be repeatedly attacked. Inspired by these motivations, we propose an attack pattern with and without memory based on randomly choosing [Formula: see text] non-hub nodes with known connectivity information. We use a network system with the Poisson and power-law degree distribution to study the network robustness after applying two failure strategies of non-hub nodes. Additionally, the critical threshold [Formula: see text] and the size of the giant component [Formula: see text] are determined for a network configuration model with an arbitrary degree distribution. The results indicate that the system undergoes a continuous second-order phase transition subject to the above attack strategies. We find that [Formula: see text] gradually tends to be stable after increasing rapidly with [Formula: see text]. Moreover, the failure of non-hub nodes with a higher degree is more destructive to the network system and makes it more vulnerable. Furthermore, from comparing the attack strategies with and without memory, the results highlight that the system shows better robustness under a non-memory based attack relative to memory based attacks for [Formula: see text]. Attacks with memory can block the system’s connectivity more efficiently, which has potential applications in real-world systems. Our model sheds light on network resilience under memory and non-memory based attacks with limited information attacks and provides valuable insights into designing robust real-world systems.

Understanding percolation phase transition behaviors in complex networks from the macro and meso-micro perspectives

“…The results suggest that the critical threshold gradually becomes stable as n increased rapidly. And also, the system exhibits better resilience under non-memory attacks compared to memory attacks [44,45].…”

Acquaintance immunization with limited knowledge of network structure

“…At the same time, most targeted immunization strategies assume full-knowledge of the network structure [18][19][20][21][22][23][24][25], and while effort has been devoted to developing algorithms for reconstructing networks [26,27], this remains a challenging area. In contrast, several approaches have attempted to understand how targeted immunization can be carried out with limited knowledge, including using a percolation based theoretical framework [28][29][30][31][32]. One particular strategy that requires minimal knowledge of the network is acquaintance immunization [2], which leverages the 'friendship paradox' [33].…”