Open-access article available at: https://www.mdpi.com/1099-4300/22/9/1029 Abstract: Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the network structure and degree distributions of artificial networks generated via either preferential attachment, an inverse variant of preferential attachment, or combinations of both network growth mechanisms. The simulations showed that network growth initially driven by preferential attachment followed by inverse preferential attachment led to densely-connected network structures (i.e., smaller diameters and average shortest path lengths), as well as degree distributions that could be characterized by non-power law distributions, analogous to the features of real-world phonological networks. These results provide converging evidence that inverse preferential attachment may play a role in the development of the phonological lexicon and reflect processing costs associated with a mature lexicon structure. Keywords: network growth; preferential attachment; inverse preferential attachment; language networks; language development This OSF project contains the following: 1. data - RData file containing the simulated networks from different growth mechanisms 2. script - R scripts for conducting the simulations, analysis and generating all figures in the manuscript 3. output - analytic results in table form are here 4. plots - figures, plots, graphs are here