We consider the problems of predicting, classifying, and annotating friends relations in friends networks, based upon network structure and user profile data. First, we document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, we explain how the problem of classifying a user pair in a social network, as directly connected or not, poses the problem of selecting and constructing relevant features. We document feature analyzers for attributes that depend only on graph attributes, those that depend on individual user demographics and set-valued attributes (e.g., interests, communities, and educational institutions), and those that depend on candidate user pairs. We then extend our data model using whole-network attributes and report machine learning experiments on learning the concept of a connected pair of friends from LiveJournal data. Finally, we develop a theory of dependent types for deriving causal explanations and discuss how this can be used to scale statistical relational learning up to our full corpus, a recent crawl of over a million records from
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