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A Network Partition-based Cluster Method for Selecting Significant Community Structure

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.