The rise of accessible real-world data creates a growing interest in effective methods for accurate classification, especially for networks with incomplete information. The intelligence community requires an understanding of a network before the team can develop a strategy to combat the adversary. These problems are typically time-sensitive; however, gathering complete and actionable intelligence is a challenging mission. An adversarys actions are secretive in nature. Crucial information is deliberately concealed. Intentionally dubious information creates problematic noise. Therefore, if an observed incomplete network can be classified as-is without delay, the network can be properly analyzed for a strategy to be devised and acted upon earlier. This thesis considers a machine learning technique for classification of incomplete networks. We examine the effects of training the model with complete and incomplete information. Observed network data and their structural features are classified into technological, social, information, and biological categories using supervised learning methods.
展开▼
