When nodes share features we can combine those features in many possible ways. One standard way is to base relationships on shared features. But there are other possibilities. Here we will apply a number of approaches to investigate the concept of distinctiveness. Distinctiveness is how easy it is to discriminate one thing from another thing. In an important sense distinctiveness is therefore a hypothesis about how the mind works. We say two things are distinctive because a mind can distinguish them. But what makes something distinctive? In this chapter, I will introduce some of the theory behind distinctiveness and then demonstrate how we can use network science to investigate distinctiveness in children’s abilities to learn words. This takes a multilayer network approach, in which we will examine many different edge types constructed of various combinations of shared and unshared features. By examining these edge types will discover how best to combine features and which feature combinations best predict early word learning.

Some people appear to learn more slowly. Could they just be learning different things? Suppose two groups of children are learning words – they have growing vocabularies – but one group acquires the list more slowly than the other. Can we use the structure of the information they learn to gain insight into whether or not they are learning different information? Small worlds are one way of measuring the structure of a community. When quantitatively defined, small worlds have a number of useful properties, including that they compare the structure of a network relative to different versions of itself, thereby providing a kind of ‘control’ network against which to benchmark a measurement. In this chapter, I discuss small worlds and several ways to evaluate them, and then use them to answer a simple question: Are children who learn to talk late just slow versions of early talkers? Or are they learning something different about the world? Along the way, I will enumerate three different approaches to explaining where structure comes from: function, formation, and emulation.