Relational learning via latent social dimensions
Relational learning via latent social dimensions
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DOI：
10.1145/1557019.1557109
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Lei Tang;Huan Liu
中科院分区：
文献类型：
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作者：
Lei Tang;Huan Liu
社交媒体，如博客，Facebook，Flickr等，以网络格式呈现数据，而不是传统的IID分布。为了解决数据实例之间的相互依赖性，已经提出了关系学习，并采用基于网络连接的集体推理进行预测。然而，社交媒体中的联系往往是多维度的。一个参与者可以出于不同的原因连接到另一个参与者，例如，校友、同事、生活在同一个城市、有着相似的兴趣等。集体推理通常不会区分这些联系。在这项工作中，我们提出了基于网络信息提取潜在的社会维度，然后利用它们作为区分学习的特征。这些社会维度描述了隐藏在网络中的参与者的不同隶属关系，并且判别学习可以自动确定哪些隶属关系与类别标签更好地对齐。当多个不同的关系与同一网络相关联时，这种方案是优选的。我们对社交媒体数据进行了广泛的实验（一个来自真实世界的博客网站，另一个来自流行的内容共享网站）。我们的模型优于基于集体推理的代表性关系学习方法，特别是当很少有标记数据可用时。该模型的灵敏度和现有方法的连接也进行了检查。
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. However, connections in social media are often multi-dimensional. An actor can connect to another actor for different reasons, e.g., alumni, colleagues, living in the same city, sharing similar interests, etc. Collective inference normally does not differentiate these connections. In this work, we propose to extract latent social dimensions based on network information, and then utilize them as features for discriminative learning. These social dimensions describe diverse affiliations of actors hidden in the network, and the discriminative learning can automatically determine which affiliations are better aligned with the class labels. Such a scheme is preferred when multiple diverse relations are associated with the same network. We conduct extensive experiments on social media data (one from a real-world blog site and the other from a popular content sharing site). Our model outperforms representative relational learning methods based on collective inference, especially when few labeled data are available. The sensitivity of this model and its connection to existing methods are also examined.