A multi-node attack scheme based on community partitioning in large scale infrastructure networks

Abstract

Node attack plays a vital role in evaluating the reliability of large scale infrastructure networks, which has attracted intensive research interests. Unfortunately, most of them focus on single node attack. Multi-node attack based on MBA (Module based Adaptive) is a promising research vector with great potential in improving efficiency. However, MBA-based node attack typically needs to repetitively consider community structures of large networks during the entire node attack process, which brings significant loss in network reliability and causes computational overheads. In light of this, we propose a novel scheme based on MBA to implement multi-node attack, which adopts the Multilevel community partitioning algorithm to overcome the above limitations. We evaluate our scheme on two artificial synthetic networks and four real-world networks with comparison to four widely used node attack schemes, and demonstrate the non-negligible superiority of our scheme over all benchmarks. Especially in the US Powergrid, our scheme can always achieve robustness around 10 times that of other schemes when deleting the same percentage of nodes. To the best of our knowledge, we are the first to achieve an MBA-based node attack scheme that only needs to execute the community partitioning algorithm once during the entire node deletion process.