Seeing Red: Locating People of Interest in Networks
MetadataShow full item record
The focus of the current research is to identify people of interest in social networks. We are especially interested in studying dark networks, which represent illegal or covert activity. In such networks, people are unlikely to disclose accurate information when queried. We present REDLEARN, an algorithm for sampling dark networks with the goal of identifying as many nodes of interest as possible. We consider two realistic lying scenarios, which describe how individuals in a dark network may attempt to conceal their connections. We test and present our results on several real-world multilayered networks, and show that REDLEARN achieves up to a 340% improvement over the next best strategy.
Showing items related by title, author, creator and subject.
Nauta, Jeremy T. (Monterey, California. Naval Postgraduate School, 2012-06);Data in online social networks can be used as a resource to locate persons of interest. The two key issues are the accuracy and the length of time to carry out the necessary categorization, correlation, and sifting. Literally ...
King, Ryan H.; Bennington, Jeffrey G. (Monterey, California. Naval Postgraduate School, 2010-03);Since the beginning of civilization, humans formed social networks under communities bound by common interest. Today the ubiquity of the Internet provides ample opportunity for these groups, once limited by geography, ...
Alderson, David; Ubiquity Staff (Association for Computing Machinery (ACM), 2009-08);Since Duncan Watts and Steve Strogatz published “Collective Dynamics of Small-World Networks” in Nature in 1998, there has been an explosion of interest in mathematical models of large networks, leading to numerous research ...