论文标题
稀疏潜在空间模型中的社区检测
Community detection in sparse latent space models
论文作者
论文摘要
我们表明，一种简单的社区检测算法源自随机块模型文献，甚至达到了一系列稀疏的潜在空间模型的一致性甚至最佳性。模型类别包括潜在的特征模型（Arxiv：0711.1146）。社区检测算法基于光谱聚类，然后通过标准化边缘计数进行局部细化。
We show that a simple community detection algorithm originated from stochastic blockmodel literature achieves consistency, and even optimality, for a broad and flexible class of sparse latent space models. The class of models includes latent eigenmodels (arXiv:0711.1146). The community detection algorithm is based on spectral clustering followed by local refinement via normalized edge counting.
