Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning...
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin...
While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomp...
—There is a growing interest in methods for analyzing data describing networks of all types, including information, biological, physical, and social networks. Typically the data ...
Abstract. With increasing usage of Social Networks, giving users the possibility to establish access restrictions on their data and resources becomes more and more important. Howev...
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 label...