As social networks become more prevalent, there is significant interest in studying these network data, the focus often being on detecting anomalous events. This area of research is referred to as social network surveillance or social network change detection. While there are a variety of proposed methods suitable for different monitoring situations, two important issues have yet to be completely addressed in network surveillance literature. First, performance assessments using simulated data to evaluate the statistical performance of a particular method. Second, the study of aggregated data in social network surveillance. The research presented tackle these issues in two parts, evaluation of a popular anomaly detection method and investigation of the effects of different aggregation levels on network anomaly detection. / Ph. D. / Social networks are increasingly becoming a part of our normal lives. These networks contain a wealth of information that can be immensely useful in a variety of areas, from targeting a specific audience for advertisement, to apprehending criminals, to detecting terrorist activities. The research presented focus evaluating popular methods on monitoring these social networks, and the potential information loss one might encounter when only limited information can be collected over a specific time period, we present our commendations on social network monitoring that are applicable to a wide range of scenarios as well.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/79849 |
| Date | 27 October 2017 |
| Creators | Zhao, Meng John |
| Contributors | Statistics, Woodall, William H., Driscoll, Anne R., Stevens, Nathaniel T., Fricker, Ronald D. Jr., Sengupta, Srijan |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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