  In this paper we provide a quantitative framework for the study of
phonological networks (PNs) for the English language by carrying out principled
comparisons to null models, either based on site percolation, randomization
techniques, or network growth models. In contrast to previous work, we mainly
focus on null models that reproduce lower order characteristics of the
empirical data. We find that artificial networks matching connectivity
properties of the English PN are exceedingly rare: this leads to the hypothesis
that the word repertoire might have been assembled over time by preferentially
introducing new words which are small modifications of old words. Our null
models are able to explain the "power-law-like" part of the degree
distributions and generally retrieve qualitative features of the PN such as
high clustering, high assortativity coefficient, and small-world
characteristics. However, the detailed comparison to expectations from null
models also points out significant differences, suggesting the presence of
additional constraints in word assembly. Key constraints we identify are the
avoidance of large degrees, the avoidance of triadic closure, and the avoidance
of large non-percolating clusters.
