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The stochastic topic block model for the clustering of vertices in networks with textual edges

机译：具有文本边缘的网络中顶点聚类的随机主题块模型

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摘要

Due to the significant increase of communications between individuals via social media (Facebook, Twitter, Linkedin) or electronic formats (email, web, e-publication) in the past two decades, network analysis has become an unavoidable discipline. Many random graph models have been proposed to extract information from networks based on person-to-person links only, without taking into account information on the contents. This paper introduces the stochastic topic block model, a probabilistic model for networks with textual edges. We address here the problem of discovering meaningful clusters of vertices that are coherent from both the network interactions and the text contents. A classification variational expectation-maximization algorithm is proposed to perform inference. Simulated datasets are considered in order to assess the proposed approach and to highlight its main features. Finally, we demonstrate the effectiveness of our methodology on two real-word datasets: a directed communication network and an undirected co-authorship network.
机译：由于过去二十年来个人之间通过社交媒体（Facebook，Twitter，Linkedin）或电子格式（电子邮件，网络，电子出版物）之间的交流显着增加，网络分析已成为不可避免的学科。已经提出了许多随机图模型来仅基于人对人链接从网络中提取信息，而不考虑内容的信息。本文介绍了随机主题块模型，这是一种具有文本边缘的网络的概率模型。我们在这里解决了从网络交互和文本内容中发现有意义的顶点簇的问题。提出了一种分类变分期望最大化算法进行推理。为了评估所提出的方法并突出其主要特征，考虑了模拟数据集。最后，我们在两个实词数据集上证明了我们的方法的有效性：定向通信网络和非定向共同作者网络。

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