Abstract: The community detection algorithm is very important for analyzing the topology and hierarchical structure of complex networks and predicting the evolution trend of complex networks.Traditional community detection algorithm does not have high accuracy and ignores the importance of network embedding.Aiming at such problems,a parameter-free community detection algorithm based on node similarity and network embedding Node2Vec method is proposed.First,we use the network embedding Node2Vec method to map network nodes into data points represented by low-dimensional vectors in Euclidean space,calculate the cosine similarity between the data points represented by the low-dimensional vector,construct a preference network according to the maximum similarity between the corresponding nodes,obtain the initial community detection,and use the maximum degree node of each initial community as a candidate node.Then we find the central node among the candidate nodes according to the average degree of the network and the average shortest path.Finally,the data points and their numbers corresponding to the central node are used as the initial centroid and cluster number,and the data represented by the low-dimensional vector are calculated by K-Means algorithm.The points are clustered,and the corresponding network nodes are divided into communities.This algorithm is a method of community division without parameters,which can independently extract parameters from the network without setting different hyper-parameters according to different networks,so that it can automatically and quickly identify the community structure of complex networks.In 8 real networks and artificial networks above,by comparing with other 5 well-known community discovery algorithms,numerical simulation experiments show that the proposed algorithm has good community discovery effect.

Key words: K-Means clustering, Network embedding, Node similarity, Parameter-free community detection, Preference network