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This work connects models for virus spread on networks with their equivalentneural network representations. Based on this connection, we propose a newneural network architecture, called Transmission Neural Networks (TransNNs)where activation functions are primarily associated with links and are allowedto have different activation levels. Furthermore, this connection leads to thediscovery and the derivation of three new activation functions with tunable ortrainable parameters. Moreover, we prove that TransNNs with a single hiddenlayer and a fixed non-zero bias term are universal function approximators.Finally, we present new fundamental derivations of continuous time epidemicnetwork models based on TransNNs.

Transmission Neural Networks: From Virus Spread Models to Neural Networks