Selection of significant community structure based on network partition-based cluster

Module (or community) structure detection, which has been successfully applied to many fields, is vital step for understand network dynamic and complex systems. Module structure not only is a crucial character of networks, but also is multi-scale. Therefore, many multi-scale module detection algorithms are proposed to resolve the problem. But a highly important issue for multi-scale methods is that of how to select crucial partitions among multi-scale network partitions so that these partitions can effectively help people to understand complex system. To solve the problem, we propose a novel partition-based hierarchical clustering to select significant network partitions. Experiments on selection of benchmark and real networks demonstrate that the new method for selecting significant partitions is very effectively. © 2019 Lavoisier. All rights reserved.