We propose a community detection method based on K-shell. Our method determines some core nodes of the graph according to the K-shell value of these nodes. These core nodes constitute a subgraph on which we use the community detection algorithm to divide the core nodes into communities. Compared to classical methods, by this way, our proposed method removes the non-core nodes which may impact the quality of the solutions, and the running time would be reduced by 45% at most because the graph scale is reduced. Then we use the idea of LPA to infer community labels for the non-core nodes. Our experiments demonstrate that our method can reach the quality of solutions of CNM algorithms on the network constructed by Planted l-partition model which is also better than it on dataset of the Zachary karate club network. Meanwhile, we run our method on large-scale datasets, which has a better performance than the CNM algorithm.
['Yang Wang', 'Liutong Xu', 'Bin Wu']
A community detection method based on K-shell