Improving the robustness of scale-free networks by maintaining community structure

Abstract

Abstract Scale-free networks, which play an important role in modelling human activities, are always suffering from intentional attacks which have serious consequences on their functionality. Degree distribution and community structure are two distinguishing characteristics of these networks considered in optimizing network robustness process recently. Since community structure is known as functional modules in some networks, modifying them during the improving network robustness process may affect network performance. We propose a preferential rewiring method to improve network robustness which not only keeps degree distribution unchanged but also preserves community structure and decreases the number of rewired edges at the same time. At first, the robustness of each community is improved by applying a smart rewiring method based on the neighbourhood of nodes. Then, relations between communities are gotten more robust with a preferential rewiring based on degree and betweenness hybrid centrality of nodes. This method was applied to several types of networks including Dolphins, WU-PowerGrid and US-Airline as real-world networks and Lancichinetti–Fortunato–Radicchi benchmark model as an artificial network with the scale-free property. The results show that the proposed method enhances the robustness of all networks against degree centrality attacks between 50% and 80% and betweenness centrality attacks between 30% and 70%. Whereas, in all cases, community structure preserved more than 50%. In comparison with previous studies, the proposed method can improve network robustness more significantly and decrease the number of rewires. It also is not dependent on the attack strategy.