An analysis of how ensembles of collective classifiers improve predictions in graphs.

We present a theoretical analysis framework that shows how ensembles of collective classifiers can improve predictions for graph data. We show how collective ensemble classification reduces errors due to variance in learning and more interestingly inference. We also present an empirical framework that includes various ensemble techniques for classifying relational data using collective inference. The methods span single- and multiple-graph network approaches, and are tested on both synthetic and real world classification tasks. Our experimental results, supported by our theoretical justifications, confirm that ensemble algorithms that explicitly focus on both learning and inference processes and aim at reducing errors associated with both, are the best performers.