Neighbors-based divisive algorithm for hierarchical analysis in networks

Hierarchical analysis for network structure can point out which communities can constitute a larger group or give reasonable smaller groups within a community. Numerous methods for discovering community in networks divide networks at only one certain granularity, which does not benefit hierarchical analysis for network structure. Hierarchical clustering algorithms are the common technique that reveals the multilevel structure in the network analysis. In this work, we give a definition for scores of edges according to the basic idea of means clustering. Based on the definition, a neighbors-based divisive algorithm named neighbor-means (NM) is proposed to detect communities in networks, especially for hierarchical analysis. The divisive algorithm repeatedly removes the edge with the highest score to obtain hierarchical partitions but can recalculate the scores of edges quickly with local recalculating strategy and crucial change-rules, which makes its complexity much lower than many divisive algorithms. In addition, when the community structure is ambiguous, benefited from superiority of the defined scores, our method achieves better results than many divisive and agglomerative algorithms. Experiments with artificial and real-world networks demonstrate the superiority of neighbor-means in detecting community structure.