Graphical Models for Relational Data: New Challenges and Solutions

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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.