随机块模型中的嵌入主题
Embedded Topics in the Stochastic Block Model
AI总结：
针对现有通信网络分析常忽略边上文本数据的问题，本文提出深度潜变量模型ETSBM，可同时实现节点聚类与聚类间主题建模，并通过合成及真实数据集验证了方法的有效性。
摘要：
电子邮件或社交网络等通信网络如今无处不在，其分析已成为一个战略性领域。在许多应用中，目标是通过考察节点及其连接来自动提取相关信息。遗憾的是，大多数现有方法都聚焦于分析边的存在与否，文本数据往往被丢弃。然而，实际上所有通信网络的边上都带有文本数据。为了考虑这一特性，本文研究了一类网络：当且仅当两个节点共享文本数据时，二者之间存在连接。我们提出了一种可处理嵌入主题的深度潜变量模型ETSBM（嵌入主题随机块模型），能够在对节点进行聚类的同时，对不同聚类之间使用的主题进行建模。ETSBM 同时扩展了随机块模型（SBM）和嵌入主题模型（ETM），二者分别是研究网络和语料库的核心模型。我们采用结合了随机梯度下降的变分贝叶斯期望最大化算法进行推断。该方法在合成数据和真实世界数据集上进行了评估。
英文摘要：
Communication networks such as emails or social networks are now ubiquitous and their analysis has become a strategic field. In many applications, the goal is to automatically extract relevant information by looking at the nodes and their connections. Unfortunately, most of the existing methods focus on analysing the presence or absence of edges and textual data is often discarded. However, all communication networks actually come with textual data on the edges. In order to take into account this specificity, we consider in this paper networks for which two nodes are linked if and only if they share textual data. We introduce a deep latent variable model allowing embedded topics to be handled called ETSBM to simultaneously perform clustering on the nodes while modelling the topics used between the different clusters. ETSBM extends both the stochastic block model (SBM) and the embedded topic model (ETM) which are core models for studying networks and corpora, respectively. The inference is done using a variational-Bayes expectation-maximisation algorithm combined with a stochastic gradient descent. The methodology is evaluated on synthetic data and on a real world dataset.
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。
