Classification and estimation in the Stochastic Blockmodel based on the empirical degrees
Classification and estimation in the Stochastic Blockmodel based on the empirical degrees
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DOI：
10.1214/12-ejs753
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发表时间：
2012-01-01
影响因子：
1.1
通讯作者：
Robin, Stephane
中科院分区：
文献类型：
--
作者：
Channarond, Antoine;Daudin, Jean-Jacques;Robin, Stephane
随机块模型[16]是异构网络数据的混合模型。与通常的统计框架不同，新节点提供了关于该模型中先前节点的额外信息。因此，度的分布集中在有条件的节点类的点。我们表明，在一个温和的假设下，分类，估计和模型选择实际上可以实现不超过经验度数据。我们提供了一个算法，能够处理非常大的网络和一致的估计基于它。特别是，我们证明了至少一个节点的误分类的概率的界，包括当类的数量增长。
The Stochastic Blockmodel [16] is a mixture model for heterogeneous network data. Unlike the usual statistical framework, new nodes give additional information about the previous ones in this model. Thereby the distribution of the degrees concentrates in points conditionally on the node class. We show under a mild assumption that classification, estimation and model selection can actually be achieved with no more than the empirical degree data. We provide an algorithm able to process very large networks and consistent estimators based on it. In particular, we prove a bound of the probability of misclassification of at least one node, including when the number of classes grows.