标题： 基于混合主题链接模型的可扩展文本和链接分析 显示英文标题

标题： Scalable Text and Link Analysis with Mixed-Topic Link Models

摘要： 许多数据集包含关于对象的丰富信息，以及它们之间的成对关系。 例如，在网站、科学论文和其他文档的网络中，每个节点的内容由一组单词组成，同时还包含到其他节点的超链接或引用。 为了对这些数据集进行推理，并做出预测和推荐，拥有能够捕捉每个节点生成文本的过程以及它们之间链接的模型是有用的。 在本文中，我们将主题建模中的经典思想与统计物理社区最近开发的一种混合成员块模型变体相结合。 该模型的优势在于，其参数，包括每篇文档的主题混合和由此产生的重叠社区，可以通过一个简单且可扩展的期望最大化算法进行推断。 我们在三个数据集上测试了我们的模型，进行了无监督的主题分类和链接预测。 对于这两个任务，我们的模型优于几种现有的最先进方法，在显著较少的计算量下取得了更高的准确性，能够在几分钟内分析包含130万个单词和4.4万个链接的数据集。 显示英文摘要

摘要： Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node and the links between them. In this paper, we combine classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes.