Clustering in networks with the collapsed Stochastic Block Model
Clustering in networks with the collapsed Stochastic Block Model
复制标题
使用折叠随机块模型在网络中进行聚类
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
N. Hurley
中科院分区：
文献类型：
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作者：
Aaron F. McDaid;T. B. Murphy;N. Friel;N. Hurley
提出了一种有效的MCMC算法来对网络中的节点进行聚类，使得网络中具有相似角色的节点被聚类在一起。这被称为块建模或块聚类。该模型是将块体参数积分出来的随机块体模型。由此产生的边缘分布定义了集群数量和集群成员的后验。从这个后验采样比从原始SBM采样更简单，因为可以避免跨维MCMC。该算法基于分配采样器。它需要对聚类数进行先验估计，从而允许聚类数直接由算法估计，而不是作为输入参数给出。合成和真实的数据被用来测试模型和算法的速度和准确性，包括估计聚类数的能力。该算法可以扩展到具有多达一万个节点和数千万条边的网络。
An efficient MCMC algorithm is presented to cluster the nodes of a network such that nodes with similar role in the network are clustered together. This is known as block-modelling or block-clustering. The model is the stochastic blockmodel (SBM) with block parameters integrated out. The resulting marginal distribution defines a posterior over the number of clusters and cluster memberships. Sampling from this posterior is simpler than from the original SBM as transdimensional MCMC can be avoided. The algorithm is based on the allocation sampler. It requires a prior to be placed on the number of clusters, thereby allowing the number of clusters to be directly estimated by the algorithm, rather than being given as an input parameter. Synthetic and real data are used to test the speed and accuracy of the model and algorithm, including the ability to estimate the number of clusters. The algorithm can scale to networks with up to ten thousand nodes and tens of millions of edges.
影响因子：
8.6
作者：
Hofman, Jake M.;Wiggins, Chris H.
通讯作者：
Wiggins, Chris H.