Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (17): 142-149.DOI: 10.3778/j.issn.1002-8331.1912-0018

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Estimating Model Posterior Inference Quality by Using Adversarial Samples to Train Graph Classifier

YU Qiancheng, YU Zhiwen, WANG Zhu   

  1. 1.School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
    2.Shaanxi Provincial Key Laboratory for Embedded System(Northwestern Polytechnical University), Xi’an 710072, China
  • Online:2020-09-01 Published:2020-08-31



  1. 1.西北工业大学 计算机学院,西安 710072
    2.陕西省嵌入式系统技术重点实验室(西北工业大学),西安 710072


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.

Key words: overlapping community detection, nonparametric Bayesian model, relational Infinite Latent Feature Model(rILFM), estimate posterior inference quality, graph convolutional neural network



关键词: 重叠社区发现, 非参数贝叶斯模型, 关系型无限潜特征模型, 参数推理质量估计, 图卷积神经网络