Dynamics of Information Propagation in Large Heterogeneous Networks
Large scale networked systems that include heterogeneous entities, eg humans and computational entities are becoming increasingly prevalent. Prominent applications include the Internet, large scale disaster relief and network centric warfare. In such systems, large heterogeneous coordinating entities exchange uncertain information to obtain and increase situation awareness. Uncertain and possibly conflicting sensor data is shared across a peer-to-peer network. Not every team member will have direct access to sensors and team members will be influenced mostly by their neighbors in the network with whom they communicate directly. In this talk I will present our work on the dynamics and emergent behaviors of belief propagation in such large networks. Unlike past work, the nodes in the networks we study are autonomous and actively fuse information they receive. Nodes can change their beliefs as they receive additional information over time.
A key property of the system is that it exhibits qualitatively different dynamics and system performance over different ranges of system parameters. In one particular range, the system exhibits behavior known as scale-invariant dynamics which we empirically find to correspond to dramatically improved system performance. I will present results on the emergent belief propagation dynamics in those systems, mathematical characterization of the systems behavior and distributed algorithms for adapting the network behaviors to steer the whole system to areas of optimized performance.