发现在线社交网络中隐藏的主题中心和权威
Discovering Hidden Topical Hubs and Authorities in Online Social Networks
摘要：
在在线社交网络中发现有影响力的用户是一个重要的问题，具有许多可能有用的应用。特别是，HITS和其他链接分析方法经常用于识别网络图和在线社交网络中的中心和权威用户。然而，这些作品在分析中没有考虑链接的主题方面。一个简单的方法来克服这一限制是首先应用主题模型来学习用户的主题，然后再应用HITS算法。在本文中，我们提出了一种新的主题模型称为枢纽和权威主题（HAT）模型结合这两个过程，以共同学习枢纽，权威和主题的兴趣。我们评估HAT对现有的几个国家的最先进的方法在两个方面：（i）建模的主题，和（ii）链接推荐。我们在Twitter和Instagram的两个真实世界的数据集上进行实验。我们的实验结果表明，HAT是国家的最先进的主题模型在学习主题，它优于国家的最先进的链接推荐任务。
英文摘要：
Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combine the two process so as to jointly learn the hub, authority and topical interests. We evaluate HAT against several existing state-of-the-art methods in two aspects: (i) modeling of topics, and (ii) link recommendation. We conduct experiments on two real-world datasets from Twitter and Instagram. Our experiment results show that HAT is comparable to state-of-the-art topic models in learning topics and it outperforms the state-of-the-art in link recommendation task.
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。
