A new algorithm CNM-Centrality of detecting communities based on node centrality

• We propose a new algorithm CNM-Centrality to discover communities based on node centrality.
• The important nodes (central nodes) are identified by PageRank algorithm.
• The convergence condition of CNM algorithm has been changed to optimize the objective function.
• SNN similarity is calculated to determine whether two communities can be merged or not.
• This new algorithm can perform well on networks with obvious centrality of community structure.

The discovery and analysis of community structure in complex networks is a hot issue in recent years. In this paper, based on the fast greedy clustering algorithm CNM with the thought of local search, the introduction of the idea of node centrality and the optimal division of the central nodes and their neighbor nodes into correct communities, a new algorithm CNM-Centrality of detecting communities in complex networks is proposed. In order to verify the accuracy and efficiency of this algorithm, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks by LFR-benchmark. The experimental results indicate that this algorithm can identify the communities accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity and NMI than the CNM, Infomap, Walktrap algorithms do.