具有文本边缘的网络中顶点聚类的随机主题块模型
The Stochastic Topic Block Model for the Clustering of Vertices in Networks with Textual Edges
摘要：
在过去的二十年里，由于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）的通信显着增加，网络分析已成为一个不可避免的学科。已经提出了许多随机图模型，以仅基于人与人之间的链接从网络中提取信息，而不考虑关于内容的信息。本文介绍了随机主题块模型（STBM），一个概率模型的网络与文本边缘。我们在这里解决的问题，发现有意义的集群的顶点是一致的，从网络的相互作用和文本内容。提出了一种分类变分期望最大化（C-VEM）算法进行推理。模拟数据集被认为是为了评估所提出的方法，并突出其主要特点。最后，我们证明了我们的方法在两个真实世界的数据集：有向通信网络和无向合著网络的有效性。
英文摘要：
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 a 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 (STBM), 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 (C-VEM) algorithm is proposed to perform inference. Simulated data sets 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 data sets: a directed communication network and a undirected co-authorship network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
