Link Mining and Discovery

这个项目研究了几个统计推理任务，这些数据最好被描述为异构链接对象的集合。对于每个任务，都提出了模型，开发了用于学习和使用模型进行推理的算法，并进行了广泛的经验评估。由于传统的统计学习算法常常做出不适合关联数据的独立性假设，因此需要新的方法来挖掘关联数据。研究的第一个任务是基于对象的属性和它所链接的对象的描述来预测对象的分类。我们正在开发有效的集体分类算法，考虑到对象分类之间的依赖性。下一个任务是使用标记和未标记的数据进行基于链接的分类。近年来，使用未标记数据来提高命题域的分类性能受到了相当大的关注；在链接数据中使用未标记数据进行预测更加有趣，因为它提供了关于对象分布和链接分布的信息。第三个任务是基于链接的聚类分析，即根据对象属性和链接属性找到相似的对象。这项工作的结果将为大型关联异构域的有效统计分析提供见解。这可以应用于发现社会网络的模式，包括犯罪和恐怖主义网络，生物数据，如流行病学研究，以及广泛的其他异构关联数据集合。

This project examines several statistical inference tasks for data best described as a collection of heterogeneous linked objects. For each task, models are proposed, algorithms for both learning and making inferences using the models are developed and extensive empirical evaluation is performed. New approaches to mining linked data are needed because traditional statistical learning algorithms often make independence assumptions that are inappropriate for linked data.The first task studied is predicting the classification of an object based on attributes of the object and on a description of the objects to which it is linked. We are developing efficient collective classification algorithms that take into account the dependence between the object classifications. The next task examined is the use of labeled and unlabeled data for link-based classification. The use of unlabeled data to improve classification performance in propositional domains has received considerable attention in recent years; the use of unlabeled data for prediction in linked data is even more interesting, as it gives information about both the object distributions and the link distributions. The third task examined is link-based cluster analysis, i.e. finding similar objects based on both the object attributes and link properties. Results from this work will provide insight into effective statistical analysis for large linked heterogeneous domains. This has applications for discovering patterns in social networks, including criminal and terrorist networks, biological data such as epidemiological studies and a wide range of other collections of heterogeneous linked data.