In this paper, we describe the challenges inherent to the task of <i>link prediction</i>, and we analyze one reason why many link prediction models perform poorly. Specifically, we demonstrate theâ€¦ (More)

Active inference seeks to maximize classification performance while minimizing the amount of data that must be labeled ex ante. This task is particularly relevant in the context of relational data,â€¦ (More)

Statistics on networks have become vital to the study of relational data drawn from areas such as bibliometrics, fraud detection, bioinformatics, and the Internet. Calculating many of the mostâ€¦ (More)

Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the <i>k</i>-medoids algorithm and the Girvan-Newmanâ€¦ (More)

made significant progress over the last 5 years. We have successfully demonstrated the feasibility of a number of probabilistic models for rela-tional data, including probabilistic relational models,â€¦ (More)

Blocking is a technique commonly used in manual statistical analysis to account for confounding variables. However, blocking is not currently used in automated learning algorithms. These algorithmsâ€¦ (More)

Testing for marginal and conditional independence is a common task in machine learning and knowledge discovery applications. Prior work has demonstrated that conventional independence tests sufferâ€¦ (More)

Algorithms for relational learning and proposi-tional learning face different statistical challenges. In contrast to propositional learners, rela-tional learners often make statistical inferencesâ€¦ (More)