Lexical networks may vary as a function of individual differences in vocabulary knowledge and word-level features. Analyses often rely on descriptive network statistics, which do not support robust inferences. This study introduces the latent space model as a method for assessing the degree to which network structure is accounted for by word-level features. We analyze lexical networks from adults with below-average vs. above-average receptive vocabulary knowledge (n = 22 per group). We used latent space models to assess effects of semantic, taxonomic, and phonological similarity between words on network structure as well as effects of part-of-speech, concreteness, age-of-acquisition, and word frequency. For both groups, we found significant effects of semantic and taxonomic similarity, with additional effects of phonological similarity and concreteness for the low vocabulary group. These findings suggest increased reliance on fewer cues in lexical networks of adults with larger vocabularies. Implications for inferential modeling of lexical networks are discussed.