We examine the nature of phonological and semantic similarity\nin early language learning. We consider how the use of this\ninformation might change over the course of development. To\nthis end, we represent the lexicon as either a phonological or\nsemantic network and model the growth of this network. Constructing\nnormative vocabularies from the Communicative Development\nInventory norms, we utilize a preferential attachment\ngrowth algorithm. We predict and quantify the words\nwhich will be learned next, comparing the two network representations.\nWe consider the effect of age, total vocabulary size\nand language ability as measured through CDI percentile. Our\nfindings suggest that the semantic representation does not outperform\nthe baseline bag-of-words model, whereas the phonological\nrepresentation conditionally does. More generally, we\nshow that the network representation influences the ability of\na model to capture vocabulary growth. We further offer a\nmethod of analysis for testing representational assumptions in\nnetwork models.