Likelihood Inference for Large Scale Stochastic Blockmodels with Covariates based on a Divide-and-Conquer Parallelizable Algorithm with Communication.
Likelihood Inference for Large Scale Stochastic Blockmodels with Covariates based on a Divide-and-Conquer Parallelizable Algorithm with Communication.
复制标题
基于分而治之的可并行通信算法的具有协变量的大规模随机块模型的似然推断。
DOI：
10.1080/10618600.2018.1554486
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Michailidis,George
中科院分区：
文献类型：
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作者：
Roy,Sandipan;Atchadé,Yves;Michailidis,George
我们考虑一个随机块模型配备了节点协变量信息，这是有助于分析社会网络数据。关键目标是获得模型参数的最大似然估计。对于这项任务，我们设计了一个快速的，可扩展的Monte Carlo EM型算法的基础上的情况下，控制近似的对数似然加上一个子采样方法。所提出的算法的一个关键特征是其并行性，通过在几个核心上处理部分数据，同时在算法的每次迭代期间利用跨核心的关键统计信息的通信。该算法的性能进行评估合成数据集和竞争的方法块模型参数估计相比。我们还说明了从Facebook派生的社交网络增强节点协变量信息的数据模型。这篇文章的补充材料可以在网上找到。
We consider a stochastic blockmodel equipped with node covariate information, that is, helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic datasets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information. Supplemental materials for this article are available online.