Link Mining and Discovery

链接挖掘和发现

• 批准号: 0308030
• 负责人: Lise Getoor
• 金额: --
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0308030&HistoricalAwards=false
• 关键词: Link; Mining; Discovery

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.

该项目检查了数据的几个统计推理任务，这些数据最好被描述为异构链接对象的集合。 对于每项任务，都提出了模型，开发了使用模型进行学习和推理的算法，并进行了广泛的实证评估。 需要新的方法来挖掘关联数据，因为传统的统计学习算法经常做出不适合关联数据的独立性假设。研究的第一个任务是根据对象的属性和对象的描述来预测对象的分类。它是相连的。 我们正在开发有效的集体分类算法，该算法考虑了对象分类之间的依赖性。 下一个检查的任务是使用标记和未标记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领域的分类性能受到了相当多的关注；使用未标记的数据在链接数据中进行预测甚至更有趣，因为它提供了有关对象分布和链接分布的信息。 检查的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查找相似对象。 这项工作的结果将为大型链接异构域的有效统计分析提供见解。 这可用于发现社交网络中的模式，包括犯罪和恐怖分子网络、流行病学研究等生物数据以及各种其他异构链接数据集合。