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Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

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Abstract

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived. However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.

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Acknowledgements

This work was partially supported by the National Basic Research Program of China (973) (2012CB316402); The National Natural Science Foundation of China (Grant Nos. 61332005, 61725205); The Research Project of the North Minzu University (2019XYZJK02, 2019XYZJK05, 2017KJ24, 2017KJ25, 2019MS002); Ningxia first-class discipline and scientific research projects (electronic science and technology, NXYLXK2017A07); NingXia Provincial Key Discipline Project-Computer Application; The Provincial Natural Science Foundation of NingXia (NZ17111, 2020AAC03219).

We thanks Keyulu Xu, MuHan Zhang and Mikkel N. Schmidt for their great research work and helpful open-source code. Thanks anonymous reviewers for their comments.

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Correspondence to Qianchen Yu.

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Qiancheng Yu received his MS degree from Beifang University of Nationalities, China in 2008. Currently, he is a PhD candidate in the School of Computer Science at Northwestern Polytechnical University, China. His research interests include socially aware computing.

Zhiwen Yu received his PhD degree from Northwestern Polytechnical University, China in 2005. Currently, he is a professor in the School of Computer Science at Northwestern Polytechnical University, China. His research interests include Pervasive computing.

Zhu Wang received his PhD degree from Northwestern Polytechnical University, China in 2013. Currently, he is aassociated professor in the School of Computer Science at Northwestern Polytechnical University, China. His research interests include Pervasive computing.

Xiaofeng Wang received his PhD degree from Guizhou University, China in 2013. Currently, he is an associate professor in the College of Computer Science and Engineering at Beifang University of Nationalities, China. His research interests include algorithm design and analysis.

Yongzhi Wang received his PhD degree from JiLin University, China in 2008. Currently, he is a professor in the College of Earth Exploration Science and Technology at Jilin University, China. His research interests include Pervasive computing.

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Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

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Yu, Q., Yu, Z., Wang, Z. et al. Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection. Front. Comput. Sci. 14, 146323 (2020). https://doi.org/10.1007/s11704-020-9370-z

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