We used the tools of network science to examine different dimensions of phonological similarity. Data from a phonological associate task and from an identification of words in noise task were used to create two separate networks. The resulting networks were compared to each other and to a network formed by a computational metric (i.e., one-phoneme metric) widely-used to assess phonological similarity using an information-theoretic approach and a variety of network measures (e.g., small-world structure, scale-free structure, mixing by degree, location of nodes in the network, and community structure). While we found that a network formed by the one-phoneme metric was structurally less similar to the network formed from the phonological associate task and to the network formed from the identification of words in noise task than the latter two were to each other, there were also several common network structure features between the one-phoneme metric network and the phonological association network. We then compared the influence of degree (equivalent to neighborhood density) from each of the networks on behavioral data, namely reaction time on visual and auditory lexical decision tasks, obtained from two psycholinguistic megastudies to provide behavioral evidence for differences in network structures. We found that the effect of degree differed across network types and tasks. We discuss the advantages and disadvantages of each approach to determining phonological similarity, the implications of using each approach, and a possible direction forward for language research through the use of multiplex networks.