Existing methods based on graph autoencoder(GAE)usually ignore the influence of multi-scale feature information of encoding layers on community detection,and get sub-optimal results due to the lack of a unified optimization objective function.To this end,a self-supervised method for community detection based on GAE with multi-scale features was presented.A multi-scale self-expression module was introduced,discriminative node representations were obtained from different encoding layers and fused.A node clustering module was introduced to obtain rough clustering results.At the same time,a self-supervised node rep-resentation learning process was introduced to achieve better results,thereby constructing an end-to-end network group discovery model.Through comparative experiments on multiple datasets,the effectiveness of the proposed method is verified,and the accuracy of community detection is significantly improved compared with that of the existing methods.
维普
万方数据