The distribution of degree in real world networks is generally highly skewed. Network researchers with different disciplinary backgrounds describe the distribution in different ways. Social scientists typically report variance, centralisation, or skewness. In contrast, mathematical physicists typically report power law exponents. In part, this difference reflects differences in the networks being examined, particularly network size, and consequent differences in distribution shape. In this presentation, I suggest that the Gini coefficient is an appropriate distribution shape measure for any network.