Learning in unlabeled networks - An active learning and inference approach

The task of determining labels of all network nodes based on the knowledge about network structure and labels of some training subset of nodes is called the within-network classification. It may happen that none of the labels of the nodes is known and additionally there is no information about number of classes (types of labels) to which nodes can be assigned. In such a case a subset of nodes has to be selected for initial label acquisition. The question that arises is: “labels of which nodes should be collected and used for learning in order to provide the best classification accuracy for the whole network?”. Active learning and inference is a practical framework to study this problem. In this paper, set of methods for active learning and inference for within-network classification is proposed and validated. The utility score calculation for each node based on network structure is the first step in the entire process. The scores enable to rank the nodes. Based on the created r...

• Type: Journal
• Year: 2015
• Where: AICOM
• Authors: Tomasz Kajdanowicz, Radoslaw Michalski, Katarzyna Musial, Przemyslaw Kazienko
• Keywords: AICOM 2015