Network Structural Transformation-Based Community Detection with Autoencoder

In this paper, we proposed a novel community detection method based on the network structure transformation, that utilized deep learning. The probability transfer matrix of the network adjacency matrix was calculated, and the probability transfer matrix was used as the input of the deep learning network. We use a denoising autoencoder to nonlinearly map the probability transfer matrix into a new sub space. The community detection was calculated with the deep learning nonlinear transform of the network structure. The network nodes were clustered in the new space with the K-means clustering algorithm. The division of the community structure was obtained. We conducted extensive experimental tests on the benchmark networks and the standard networks (known as the initial division of communities). We tested the clustering results of the different types, and compared with the three base algorithms. The results showed that the proposed community detection model was effective. We compared the results with other traditional community detection methods. The empirical results on datasets of varying sizes demonstrated that our proposed method outperformed the other community detection methods for this task.