Detecting network overlapping community has become a very hot research topic in the literature. However, overlapping community detection for count-value networks that naturally arise and are pervasive in our modern life, has not yet been thoroughly studied. We propose a generative model for count-value networks with overlapping community structure and use the Indian buffet process to model the community assignment matrix Z; thus, provide a flexible nonparametric Bayesian scheme that can allow the number of communities K to increase as more and more data are encountered instead of to be fixed in advance. Both collapsed and uncollapsed Gibbs sampler for the generative model have been derived. We conduct extensive experiments on simulated network data and real network data, and estimate the inference quality on single variable parameters. We find that the proposed model and inference procedure can bring us the desired experimental results.

中文翻译：

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计数值网络的社区检测重叠

检测网络重叠社区已经成为文献中非常热门的研究课题。但是，对于在我们的现代生活中自然产生并普遍存在的计数值网络的重叠社区发现，尚未进行深入研究。我们提出了具有重叠社区结构的计数值网络的生成模型，并使用Indian Buffet过程对社区分配矩阵Z进行建模。因此，提供一种灵活的非参数贝叶斯方案，该方案可以允许社区数K随着遇到越来越多的数据而增加，而不是预先确定。生成模型的折叠和未折叠的Gibbs采样器均已导出。我们对模拟网络数据和真实网络数据进行了广泛的实验，并估计了单个变量参数的推理质量。我们发现，提出的模型和推理过程可以为我们带来理想的实验结果。