Deep community detection in topologically incomplete networks

In this paper, we consider the problem of detecting communities in topologically incomplete networks (TIN), which are usually observed from real-world networks and where some edges are missing. Existing approaches to community detection always consider the input network as connected. However, more or less, even nearly all, edges are missing in real-world applications, e.g. the protein–protein interaction networks. Clearly, it is a big challenge to effectively detect communities in these observed TIN. At first, we bring forward a simple but useful method to address the problem. Then, we design a structured deep convolutional neural network (CNN) model to better detect communities in TIN. By gradually removing edges of the real-world networks, we show the effectiveness and robustness of our structured deep model on a variety of real-world networks. Moreover, we find that the appropriate choice of hop counts can improve the performance of our deep model in some degree. Finally, experimental results conducted on synthetic data sets also show the good performance of our proposed deep CNN model.