This paper examines the emergence of a pattern that Stump and Finkel () dub Marginal Detraction: a tendency in inflection class systems for low type frequency (i.e., irregular) classes to disproportionately detract from the predictability of regular classes. We ask: What factors lead to the emergence (and sometimes non-emergence) of Marginal Detraction? We use an iterated agent-based Bayesian learning model to simulate the conditions for analogical restructuring of inflection classes over time. Input to the model consists of artificial inflection class systems that vary in how the classes overlap — their network structure. We find that network properties predict whether the Marginal Detraction distribution emerges within the model. We conclude that languagespecific network properties shape local interactions among words and thereby likely play a significant role in analogical inflection class restructuring and the emergence (or non-emergence) of global properties of inflectional systems.