  Traffic dynamics is universally crucial in analyzing and designing almost any
network. This article introduces a novel theoretical approach to analyzing
network traffic dynamics. This theory's machinery is based on the notion of
traffic divergence, which captures the flow (im)balance of network nodes and
links. It features various analytical probes to investigate both spatial and
temporal traffic dynamics. In particular, the maximal traffic distribution in a
network can be characterized by spatial traffic divergence rate, which reveals
the relative difference among node traffic divergence. To illustrate the
usefulness, we apply the theory to two network-driven problems: throughput
estimation of data center networks and power-optimized communication planning
for robot networks, and show the merits of the proposed theory through
simulations.
