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Numerous real-world applications produce networked data such as web data (hypertext documents connected via hyperlinks) and communication networks (people connected via communication links). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such data. In this report, we(More)
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with " flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much of the relational structure present in our database. This paper builds on(More)
A key challenge for machine learning is tackling the problem of mining richly structured data sets, where the objects are linked in some way due to either an explicit or implicit relationship that exists between the objects. Links among the objects demonstrate certain patterns, which can be helpful for many machine learning tasks and are usually hard to(More)
Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks(More)
The dynamic nature of citation networks makes the task of ranking scientific articles hard. Citation networks are continually evolving because articles obtain new citations every day. For ranking scientific articles, we can define the popularity or prestige of a paper based on the number of past citations at the user query time; however, we argue that what(More)
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with " flat " data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of(More)
We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the labels of linked nodes are correlated, and the goal is to exploit these dependencies and accurately label the nodes. This problem arises in many domains, including social and biological(More)
In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and(More)