Overlapping community detection is a key research problem in social network analysis and mining, most of the existing methods require to predefine the number of communities K manually, this will cause a lot of issues. By extending the infinite latent feature model to relational data and building a network generation model based on the nonparametric Bayesian hierarchy model, setting the value of K in advance can be avoided. However, posteriori inference of the relational infinite latent feature model is a probability distribution over a binary matrices. How to summarize the posteriori inference result and estimate the quality of the posteriori inference for such multivariable structural parameter is a challenge. Therefore, a method is proposed which using adversarial samples to train the graph classifier to help to summarize the inference result and to estimate the quality of the inference via the adversarial training graph convolutional neural network.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1912-0018