Graphical Models for Relational Data: New Challenges and Solutions

数据通常以对象和关系的形式出现：例如，图书馆由相互引用的书籍组成;蛋白质根据各种模式与其他蛋白质结合;在线客户网络由人们组成，指出哪些其他客户提供可靠的产品推荐。这种关系可以用来预测每个对象的行为和属性。例如，如果一篇特定的新闻文章引用了几篇体育文章，这就证明了这篇特定的文章很可能是关于体育的。我们提出了探索这种关系信息的新方法。第一个任务是准确地预测对象的属性（例如，新闻文章的类）基于与其共享关系的其它对象（例如，被我们的目标引用的其他文章）。我们发现，有一些重要的形式的关系，没有得到妥善处理，通过目前的方法，并提出了一种新的方法来说明这种关系。第二个任务集中在关系结构相似性的度量方法上。例如，如果我们知道两个蛋白质在酵母细胞内相互作用，我们是否可以推断其他蛋白质对以类似的方式连接？我们展示了如何使用概率模型来制定这样的问题，并开发了在关系数据中发现模式的新方法，并将其应用于各种现实问题。

Data often come under the form of objects and relationships: forinstance, a library consists of books that cite each other; proteinsbind to other proteins according to a variety of patterns; a networkof online customers is formed by people that indicate which othercustomers give reliable product recommendations. Such relationshipscan be used to predict the behavior and properties of each object. Forinstance, if a particular news article cites several sport articles,this is evidence that the particular article is likely to be aboutsports. We propose novel ways of exploring this relationalinformation. The first task is precisely how to predict the propertiesof an object (e.g., the class of a news article) based on otherobjects that that share a relationship with it (e.g., the otherarticles that are cited by or cite our target). We show that thereare important forms of relationship that are not properly treated bycurrent methods, and propose a new methodology to account for suchrelations. The second task focuses on ways to measure similarity ofrelational structures. For instance, if we know that two proteinsphysically interact inside a yeast cell, can we infer which otherpairs of proteins are linked in a similar way? We show how toformulate problems like this using probabilistic models, and developnovel ways of discovering patterns in relational data withapplications to a variety of real-world problems.