Complex networks (both natural and engineered) arise as a result of local interactions between various agents. The efficacy of these networks is often predicated on their ability to diffuse information throughout the network, allowing the agents to reach agreement on an appropriate quantity of interest. In this context, a key metric is the susceptibility (or resiliency) of the network to a few individuals who wish to affect global decisions via their local actions. In this talk, I provide an overview of some recent approaches to analyzing the dynamics of information propagation in networks. I describe how tools from Markov chain theory, linear system theory, and structured system theory can be applied to analyze global behaviour arising from a certain class of linear dynamics, and examine the effect of the network's topology on its resilience to misbehaving agents. I conclude by highlighting some areas for further research.

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