This chapter reviews approaches to modeling semantic lexicon growth through network science. These include modeling child lexicon growth, predicting word learning order using semantic distinctiveness, predicting word learning order using contextual diversity, exploring lexical structure differences in atypical development, and exploring differences in the developing network structure of bilinguals. Patterns consistent with preferential attachment have already been found in nonlinguistic domains, and Steyvers and Tenenbaum proposed such a model for lexical growth over cultural or developmental time scales, providing a useful hypothesis for early word learning. The preferential attachment model was not any better than a model based on random word learning. This can be in turn correlated with age of acquisition of the nodes, examining the relationship between feature similarity and word learning.