This document discusses alternatives to power law distributions for modeling networks. It presents the zeta distribution and normalized entropy as alternatives that can model networks with similar accuracy to power laws but are faster to compute. It also discusses using the Gini coefficient to measure inequality in networks as an alternative to power law fitting. These alternative methods are evaluated based on their generality, interpretability, runtime and ability to model all nodes in a network.