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The dynamic stochastic topic block model for dynamic networks with textual edges

机译：具有文本边缘的动态网络的动态随机主题块模型

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摘要

The present paper develops a probabilistic model to cluster the nodes of a dynamic graph, accounting for the content of textual edges as well as their frequency. Vertices are clustered in groups which are homogeneous both in terms of interaction frequency and discussed topics. The dynamic graph is considered stationary on a latent time interval if the proportions of topics discussed between each pair of node groups do not change in time during that interval. A classification variational expectation-maximization algorithm is adopted to perform inference. A model selection criterion is also derived to select the number of node groups, time clusters and topics. Experiments on simulated data are carried out to assess the proposed methodology. We finally illustrate an application to the Enron dataset.
机译：本文开发了一种概率模型来聚类动态图的节点，考虑了文本边缘的内容及其频率。顶点聚集在各个方面，在交互频率和讨论的主题方面均是同质的。如果每对节点组之间讨论的主题的比例在该时间间隔内未随时间变化，则该动态图被视为在潜在时间间隔内处于静止状态。采用分类变分期望最大化算法进行推理。还可以得出模型选择标准来选择节点组，时间集群和主题的数量。对模拟数据进行实验以评估所提出的方法。最后，我们说明了对Enron数据集的应用。

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