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This talk focuses on a quantification of reputation and presents models which compute reputation within networked environments. Reputation manifests past behaviors of users and helps others to predict behaviors of users and therefore reduce risks in future interactions. There are two approaches in computing reputation on networks- namely, the macro-level approach and the micro-level approach. A macro-level assumes that there exists a computing entity outside of a given network who can observe the entire network including degree distributions and relationships among nodes. In a micro-level approach, the entity is one of the nodes in a network and therefore can only observe the information local to itself, such as its own neighbors behaviors.
Jooyoung Lee is an assistant professor at the Innopolis University, Russia. Her research areas are social network analysis, reputation management, bayesian game theory and multi-agent systems. Her research specifically focuses on distributed reputation computation and estimating degrees of nodes in online social network. She was a visiting scientist at KAIST (Korea Advanced Institute of Science and Technology) and she is currently at the Polytechnic University of Milan. She received her Ph.D in Computer Science at the Syracuse University, USA, in 2014.