Abstract
Community detection can discover the cluster structure hidden in complex networks, which helps people predict network behavior and understand network functions. It is one of the current research hotspots. In this paper, we propose a Markov similarity enhancement method, which obtains the steady-state Markov similarity enhancement matrix through the Markov iterative state transition of the initial network. According to the Markov similarity index, the network is divided into initial community structure. Then, we merge the small communities into its closely connected communities to obtain the final community. On seven real networks and artificial networks with variable parameters, compared with other seven well-known community detection algorithms, numerical simulation experiments show that the proposed algorithm has a good community detection effect.
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