Imbalance problem in community detection

Community detection gives us a simple way to understand complex networks' structures. However, there is an imbalance problem in community detection. This paper first introduces the imbalance problem and then proposes a new measure to alleviate the imbalance problem. In addition, we study two variants of the measure and further analyze the resolution scale of community detection. Finally, we compare our approach with some state of the art methods on random networks as well as real-world networks for community detection. Both the theoretical analysis and the experimental results show that our approach achieves better performance for community detection. We also find that our approach tends to separate densely connected subgroups preferentially.