GFN: An improved Fast-Newman clustering algorithm in complex networks based on the group concept

To deal with the problem that the object function of existing optimized clustering algorithms are biased, which may affect the accuracy of the clustering, the concept of groups was proposed in this paper, to model the local context of nodes during the clustering process. An improved modularity function based on the concept of groups was given, and the GFN, a clustering algorithm derived from the well-known Fast-Newman algorithm. Experiments on different datasets showed that the new method increased the clustering accuracy by 70% on average compared with the original version, proving that the group concept is significant in depicting the actual clustering structures in real networks.