RefineDCN: An Improved Community Detection Algorithm Based on Center Finding

Detecting community structure is an important problem in complex networks. Recently, the community detection method based on centers and neighbors (DCN) has been proposed, which is divided into two stages: community center point detection and label propagation. It has a better result on community detection of simple undirected graph than other algorithms. However, when there are two community centers connected directly, the method of DCN fails to find both centers. Based on the DCN algorithm, this paper proposes an optimization method based on visually-aided user interactions. By showing the local structure of the discovered center point through the force-directed layout, the potential community centers that are missing in DCN can be detected. We further propose the hierarchical visual clustering to assist users to detect more community centers easier. In addition, to make the propagation of labels more stable, we propose the multi-label propagation strategy based on importance which also preserves the labels proportion during propagation. The experimental results on both artificial and real-world networks demonstrate that our improved algorithm RefineDCN obtains better community detection results than the DCN algorithm.