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.

该项目研究了最佳数据的几个统计推理任务，以描述为异类链接对象的集合。 对于每个任务，都提出了模型，开发了用于学习和推断模型的算法，并进行了广泛的经验评估。 需要新的采矿链接数据的方法，因为传统的统计学习算法通常使独立性假设不适合链接数据。研究的第一个任务是根据对象的属性以及对其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，以考虑对象分类之间的依赖性。 检查的下一个任务是使用标记和未标记的数据用于基于链接的分类。 近年来，使用未标记的数据来改善命题领域中的分类性能。在链接数据中使用未标记的数据进行预测更加有趣，因为它提供了有关对象分布和链接分布的信息。 研究的第三个任务是基于链接的集群分析，即基于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链接的异质域提供有效的统计分析的见解。 这有针对在社交网络中发现模式的应用，包括犯罪和恐怖网络，流行病学研究等生物学数据以及其他各种异质链接数据。