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    <abstract>Diffusion processes are instrumental in describing the movement of a continuous quantity in a generic network of interacting agents. Here, we present a probabilistic framework for diffusion in networks and propose to classify agent interactions according to two protocols where the total network quantity is conserved or variable. For both protocols, our focus is on asymmetric interactions between agents involving directed graphs. Specifically, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree Laplacian and the weighted out-degree Laplacian. We show how network diffusion can be externally manipulated by applying time-varying input functions at individual nodes. The network control and design schemes enable flow modifications that allow the alteration of the dynamic and stationary behavior of the network in conservative and non-conservative networks. The proposed framework is relevant in the context of group coordination, herding behavior, distributed algorithms, and network control.</abstract>
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