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School of Electrical, Computer and Energy Engineering

S. Phillips

Published 2014 in 2014 EDI Proceedings

ABSTRACT

Our ability to understand networks is important to many applications, from the analysis and modeling of interactions between biological networks, to man-made social and infrastructure systems networks. Unveiling the network structure and dynamics allows us to make predictions and control decisions for these organisms/systems. At a higher abstraction level, the dynamic system models learnt have inspired new ideas for computation methods involving multi-agents cooperation, offering effective ways for solving information processing problems. This dissertation presents new results on the intertwined problems of networks inference (or identification) and multi-agents optimization. The presentation is divided into two parts — The first part deals with the modeling and identification of network dynamics. We study two types of network dynamics arising from social networks and gene networks. The dynamics models are described in a generic form and their corresponding steady states are characterized. Our network identification method is akin to realizing a ‘network

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