A two-level learning strategy based memetic algorithm for enhancing community robustness of networks

- A community robustness index is used to evaluate the integrality of the community structure.
- We propose a memetic algorithm to enhance the community robustness of networks.
- The two-level learning strategies are designed to mitigate the multilevel targeted attacks.
- Experimental results show the effectiveness and the stability of the proposed algorithm.

Community structure is a natural and inherent property of complex networks which can reflect their potential functionality. When the robustness of a network is improved, its community structure should be preserved as much as possible. However, most earlier studies only considered enhancing the network robustness and ignored the analysis of the community structure, which may alter the original topological structure and functionality of networks. In this paper, we propose a new memetic algorithm (MA-CR) with a two-level learning strategy to enhance the community robustness of networks, while maintaining the degree distribution and community structure. The proposed MA-CR is a hybrid global-local heuristic search methodology which adopts genetic algorithm as the global search and the proposed two-level learning strategy as the local search. The two-level learning strategy is designed based on the potential characteristics of the node structure and community structure of networks, which aims at mitigating two-level targeted attacks. Experiments on synthetic scale-free networks as well as real-world networks demonstrate the effectiveness and stability of the proposed algorithm as compared with several state-of-the-art algorithms.