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We study the long-run emergence of behavioral patterns in dynamic complex net- works. Individuals can display two kinds of behavior: G (good) or B (bad). We assume that the exposure of a G agent to bad behavior on the part of peers/neighbors triggers her own switch to B behavior, but only temporarily. We model the implications of such peer eects as an epidemic process in the standard SIS (Susceptible-Infected- Susceptible) framework. The key novelty of our model is that, unlike in the received literature, the network is taken to change over time within the same time scale as be- havior. Specically, we posit that links connecting two G agents last longer, reecting the idea that B agents tend to be avoided. The main concern of the paper is to under- stand the extent to which such biased network turnover may play a signicant role in supporting G behavior in a social system. And indeed we nd that network coevolution has nontrivial and interesting eects on long-run behavior. This yields fresh insights on the role of (endogenous) peer pressure on the diusion of (a)social behavior and also has some bearing on the traditional study of disease epidemics.