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.

这个项目研究了几个统计推断任务，以最好地将数据描述为异类链接对象的集合。对于每个任务，提出了模型，开发了使用这些模型进行学习和推理的算法，并进行了广泛的经验评估。挖掘关联数据需要新的方法，因为传统的统计学习算法经常做出不适用于关联数据的独立假设。研究的第一个任务是根据对象的属性和对对象的描述来预测对象的分类。我们正在开发高效的集合分类算法，该算法考虑了对象分类之间的依赖关系。研究的下一个任务是使用标记数据和未标记数据进行基于链接的分类。近年来，使用无标签数据来提高命题领域的分类性能受到了相当大的关注；使用无标签数据在关联数据中进行预测更加有趣，因为它提供了关于对象分布和链接分布的信息。研究的第三个任务是基于链接的聚类分析，即基于对象属性和链接属性来查找相似对象。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洞察力。这可应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其他各种不同关联数据的广泛收集。