Social networks are naturally represented as heterogeneous networks with multiple types of objects e.g., actors, items and multiple types of links e.g., links between actors that denote social ties e.g., friendship, and links that connect actors to items e.g., photos, videos, articles, etc. that denote relationships between actors and items. In this paper, we consider the task of assigning labels to the unlabeled actors (individuals) in a large heterogeneous social network in which labels are available for a subset of actors. Specifically, we seek to learn a predictive model to label actors based on the attributes of the actors themselves and/or items that are linked to them in the network. Unfortunately, the number of distinct items, represented in realworld networks such as Facebook or Flickr is quite large (in the millions) although only a small subset of them are linked to specific actors. This leads to data sparsity which causes overfitting and hence poor performance in predicting the labels of unlabeled actors. To address this problem, we induce hierarchical taxonomies over items and use the resulting taxonomies as a basis for selecting abstract and hence parsimonious representations of network data for learning the predictive models. Our experiments using three different predictors (Iterative classification Naïve Bayes, Iterative classification Logistic Regression, and EdgeCluster) on two real-world data sets, Last.fm and Flickr, show that the predictive models that take advantage of abstract representations of network data are competitive with, and in some cases, outperform those that do not.