The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email
The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email
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发表时间：
2005
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通讯作者：
A. McCallum;A. Corrada-Emmanuel;Xuerui Wang
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作者：
A. McCallum;A. Corrada-Emmanuel;Xuerui Wang
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，但不是这些链接上的语言内容或主题。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。该模型基于潜在狄利克雷分配和作者-主题（AT）模型，增加了主题分布明显取决于发送者和顺应者的关键属性，即根据人与人之间的关系引导主题的发现。我们给安然电子邮件语料库和研究人员的电子邮件档案的结果，提供证据，不仅明确相关的主题被发现，但ART模型更好地预测人们的角色。
Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the 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 the directionsensitive messages sent between entities. The model builds on Latent Dirichlet Allocation 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.