Active Learning and Inference Method for Within Network Classification

In relational learning tasks such as within network classification the main problem arises from the inference of nodes' labels based on the the ground true labels of remaining nodes. The problem becomes even harder if the nodes from initial network do not have any labels assigned and they have to be acquired. However, labels of which nodes should be obtained in order to provide fair classification results? Active learning and inference is a practical framework to study this problem. The method for active learning and inference in within network classification based on node selection is proposed in the paper. Based on the structure of the network it is calculated the utility score for each node, the ranking is formulated and for selected nodes the labels are acquired. The paper examines several distinct proposals for utility scores and selection methods reporting their impact on collective classification results performed on various real-world networks.