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 capture with traditional statistical models. Recently there has been a surge of interest in this area, fueled largely by interest in web and hypertext mining, but also by interest in mining social networks, bibliographic citation data, epidemiological data and other domains best described using a linked or graph structure. In this paper we propose a framework for modeling link distributions, a link-based model that supports discriminative models describing both the link distributions and the attributes of linked objects. We use a structured logistic regression model, capturing both content and links. We systematically evaluate several variants of our link-based model on a range of data sets including both web and citation collections. In all cases, the use of the link distribution improves classification accuracy.