Batch Mode Active Learning for Networked Data

We study a novel problem of batch mode active learning for networked data. In this problem, data instances are connected with links and their labels are correlated with each other, and the goal of batch mode active learning is to exploit the link-based dependencies and node-specific content information to actively select a batch of instances to query the user for learning an accurate model to label unknown instances in the network. We present three criteria (i.e., minimum redundancy, maximum uncertainty, and maximum impact) to quantify the informativeness of a set of instances, and formalize the batch mode active learning problem as selecting a set of instances by maximizing an objective function which combines both link and content information. As solving the objective function is NP-hard, we present an efficient algorithm to optimize the objective function with a bounded approximation rate. To scale to real large networks, we develop a parallel implementation of the algorithm. Experimental results on both synthetic datasets and real-world datasets demonstrate the effectiveness and efficiency of our approach.