Graph Embedding Based on Feature Propagation for Community Detection

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.