Before revision:  We characterize the structure of the set of selfconfirming equilibria in network games and we relate selfconfirming and Nash equilibria . Thus, we provide conditions on the network under which the Nash equilibrium concept has a learning foundation, despite the fact that agents may have incomplete information. In particular, we show that the choice of being active or inactive in a network is crucial to determine whether agents can make correct inferences about the payoff state and hence play the best reply to the truth in a selfconfirming equilibrium. We also study learning dynamics and show how agents can get stuck in non--Nash selfconfirming equilibria. In such dynamics, the set of inactive agents can only increase in time, because once an agent finds it optimal to be inactive, she gets no feedback about the payoff state, hence she does not change her beliefs and remains inactive .
After revision:  We characterize the structure of the set of selfconfirming equilibria in network games and we relate selfconfirming and Nash equilibria , and we analyze simple conjectural best-reply paths whose limit points are selfconfirming equilibria .