This work has to do with the application of Network Science to study several aspects of a degenerative disease: the Alzheimer. Alzheimer’s Disease irremediably alters the proficiency of word search and retrieval processes even at its early stages. Such disruption can sometimes be paradoxical in specific language tasks, for example semantic priming. In this study, we focus on the striking side-effect of hyperpriming in Alzheimer’s Disease patients, which has been well-established in the literature for a long time. Previous studies have evidenced that modern network theory can become a powerful complementary tool to gain insight in cognitive phenomena. In our work, it is first shown that network modeling is an appropriate approach to account for semantic priming in normal subjects. Then we turn to priming in degraded cognition: hyperpriming can be readily understood in the context of a progressive degradation of the semantic network structure. We compare our simulation results with previous empirical observations in diseased patients finding a good qualitative agreement. The network approach presented here can be used to accommodate current theories about impaired cognition, and towards a better understanding of lexical organization in healthy and diseased patients.