Mixed Membership Stochastic Blockmodels
Mixed Membership Stochastic Blockmodels
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DOI：
10.5555/1390681.1442798
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发表时间：
2007-05
期刊：
影响因子：
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通讯作者：
E. Airoldi;D. Blei;S. Fienberg;E. Xing
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文献类型：
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作者：
E. Airoldi;D. Blei;S. Fienberg;E. Xing
在许多情况下，对成对对象的关系进行测量的观察出现，例如蛋白质相互作用和基因调控网络，作者-收件人电子邮件的收集和社交网络。用概率模型分析这类数据可能很微妙，因为许多样板模型所依据的简单互换性假设不再成立。在本文中，我们描述了这类数据的一个潜在变量模型，称为混合隶属度随机块模型。该模型将关系数据的块模型扩展为捕获混合成员关系潜在关系结构的块模型，从而提供特定于对象的低维表示。针对快速近似后验推理，提出了一种通用变分推理算法。我们探索社会和蛋白质相互作用网络的应用。
Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.