Provided by: Microsoft Research
Date Added: May 2013
A typical assumption in network classification methods is that the full network is available to both learn the model and apply the model for prediction. Often this assumption is appropriate (publicly visible friendship links in social networks), however in other domains, while the underlying relational structure exists, there may be a cost associated with acquiring the edges. In this preliminary paper the authors explore the problem domain of active sampling - where their goal is to maximize the number of positive (e.g., fraudulent) nodes identified, while simultaneously querying for network structure that is likely to improve estimates.