Deep Learning-Based Dynamic Community Discovery

Recurrent neural networks (RNNs) have been effective methods for time series analyses. The network representation learning model and method based on deep learning can excellently analyze and predict the community structure of social networks. However, the node relationships of complex social networks in the real world often change over time. Therefore, this study proposes a dynamic community discovery method based on a recurrent neural network, which includes (1) spatio-temporal structure reconstruction strategy; (2) spatio-temporal feature extraction model; (3) dynamic community discovery method. Recurrent neural networks can be used to obtain the time features of the community network and help us build the network time feature extraction model. In this study, the recurrent neural network model is introduced into the time series feature learning of dynamic networks. This research constructs a network spatiotemporal feature learning model combining RNN, convolutional neural networks (CNN), and auto-encoder (AE), and then uses it to explore the dynamic community structure on the spatiotemporal feature vector. The experiment chose the Email-Enron data set of the Stanford Network Analysis Platform (SNAP) website to evaluate the method. The experimental results show that the proposed method has higher modularity than Auto-encoder in the dynamic community discovery of the real social network data set. Therefore, the dynamic community discovery method based on the recurrent neural network can be applied to analyze social networks, extract the time characteristics of social networks, and further improve the modularity of the community structure.