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The Dynamics of Active Sensing in Social Networks
Abstract
Large scale social networks present unique challenges from a statistical signal processing point of view since agents interact with and influence other agents. Also agents reveal decisions and not private observations. There is strong motivation to construct models that capture the interacting dynamics of multiple agents in social networks, together with algorithms that can be used to estimate events of interest. This lecture is comprised of two parts: The first part considers Bayesian social learning models for the interaction of agents. Extensions to Bayesian data incest management in online reputation systems will be described. The second part of the talk deals with models for the propagation of information in large scale social networks modelled as random graphs. Examples include the spread of information on social media, localization and tracking using social networks. The aim is to give the audience an understanding of recent results in the signal processing of social networks and multi-agent systems.