A complex network community detection algorithm based on label propagation and fuzzy C-means.

Abstract Community detection algorithms have important significance in the research and practical application of complex network theory. This paper proposes a community detection method by improved label propagation and fuzzy C-means. Due to low accuracy and instability detection results, we modify original label propagation framework. Primarily, initial labels of vertexes are assigned by neighbor evaluation method. Secondarily, the labels of vertexes with large diversity in each community are revised by fuzzy C-means membership vectors. Tertiarily, parameters are updated until communities status is stabilized ultimately. The results showed that this method can achieve better accuracy on synthetic and real network. Highlights • Research on complex network community detection algorithm based on improved label propagation and fuzzy C-means. • The algorithm completes initialization classification of communities by neighbor evaluation method. • The algorithm adjusts the labels of unstable vertexes by fuzzy C-means membership vectors. • The accuracy of community detection is improved on synthetic and real network. [ABSTRACT FROM AUTHOR]