Community detection is one of the most important contents of complex network research, and it faces the challenge of balancing accuracy and efficiency. In response to this challenge, the paper proposes a community detection algorithm based on feature propagation. The algorithm first randomly initializes a vector for each node of the graph to complete the feature initialization. Then, with the help of the node similarity matrix, the vector representation of each node in the graph is learned through feature propagation, and finally the K-means clustering algorithm is used to cluster to obtain the community detection result. We have conducted experiments on real datasets and LFR datasets with the metric of NMI, and the results show that our algorithm is accurate and efficient.

Graph embedding models, also known as network representation models, have been tried to be applied to community detection tasks.However, most existing graph embedding models are not specially designed for community detection tasks and thus may be incapable of revealing the community structures in networks well.To fill this gap, this paper proposes two novel graph embedding models, GEMod and GEMap, which are specially designed for community detection.The proposed methods try to optimize the modified modularity and two-level coding length while learning the nodes embedding, so that the learned nodes embedding can be better applied to detect community structures in networks.Experimental results show that the algorithms proposed are superior or comparable to other community detection algorithms based on graph embedding models.Besides, the nodes embedding generated by GEMod and GEMap are generally more compact and separable, which means that they are more suitable for clustering tasks.