Topic and role discovery in social networks with experiments on enron and academic email
Topic and role discovery in social networks with experiments on enron and academic email
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DOI：
10.1613/jair.2229
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发表时间：
2007-01-01
影响因子：
5
通讯作者：
Corrada-Emmanuel, Andres
中科院分区：
文献类型：
--
作者：
McCallum, Andrew;Wang, Xuerui;Corrada-Emmanuel, Andres
社会网络分析（SNA）中以前的工作已经模拟了从一个实体到另一个实体的链接的存在，但没有这些链接上的属性，如语言内容或主题。本文提出了一种用于社会网络分析的作者-主题-主题（ART）模型，该模型根据实体之间发送的方向敏感消息来学习主题分布。该模型建立在潜在狄利克雷分配（LDA）和作者-主题（AT）模型的基础上，增加了主题分布明显取决于发送者和接收者的关键属性-根据人与人之间的关系引导主题的发现。我们给出了安然电子邮件语料库和研究人员的电子邮件档案的结果，不仅提供了证据，明确相关的主题被发现，但ART模型更好地预测人们的角色，并给出较低的困惑以前看不见的消息。我们还提出了角色作者收件人主题（RART）模型，扩展到艺术，明确表示人的角色。
Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient - steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher's email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles and gives lower perplexity on previously unseen messages. We also present the Role-Author-Recipient-Topic (RART) model, an extension to ART that explicitly represents people's roles.