Incorporating network structure with node contents for community detection on large networks using deep learning

Community detection is an important task in social network analysis. In community detection, in general, there exist two types of the models that utilize either network topology or node contents. Some studies endeavor to incorporate these two types of models under the framework of spectral clustering for a better community detection. However, it was not successful to obtain a big achievement since they used a simple way for the combination. To reach a better community detection, it requires to realize a seamless combination of these two methods. For this purpose, we re-examine the properties of the modularity maximization and normalized-cut models and fund out a certain approach to realize a seamless combination of these two models. These two models seek for a low-rank embedding to represent of the community structure and reconstruct the network topology and node contents, respectively. Meanwhile, we found that autoencoder and spectral clustering have a similar framework in their low- rank matrix reconstruction. Based on this property, we proposed a new approach to seamlessly combine the models of modularity and normalized-cut via the autoencoder. The proposed method also utilized the advantages of the deep structure by means of deep learning. The experiment demonstrated that the proposed method can provide a nonlinearly deep representation for a large-scale network and reached an efficient community detection. The evaluation results showed that our proposed method outperformed the existing leading methods on nine real-world networks.