Community Detection Based on Markov Similarity Enhancement and Network Embedding

Abstract

Community structure is ubiquitous in various complex networks in nature and is one of the important characteristics of network structure.Community detection can identify useful information in the network,and help to analyze the structure and function of the network.It is widely used in social networks,biology,medicine and other fields.Aiming at the low accuracy of the current community detection algorithm based on local similarity in complex networks,a community detection algorithm based on Markov similarity enhancement and network embedding is proposed.Firstly,inspired by the idea of Markov chain,a Markov similarity enhancement method is proposed,which obtains the steady-state Markov similarity enhancement matrix through the Mar-kov iterative state transition of the initial network.According to the Markov similarity index,the network is divided into initial community structure.Then,a new community similarity index is proposed by combining the network topology and network embedding.The small community in the initial community structure is merged with its closely connected community to obtain the network community structure.On 7 real networks and artificial networks with variable parameters,compared with other 5 well-known community detection algorithms,it is proved that the proposed algorithm has a good community detection effect.

Keywords

• community detection|complex network|markov similarity|network embedding|community similarity