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We consider a coalition network where multiple groups are interconnected via wireless links. Gateway nodes are designated by each domain to achieve a network-wide interoperability. Due to the inter-domain communication cost, the optimal gateway selection for one single domain depends on the gateway selections of other domains and vice versa. In this paper, we investigate the interactions of gateway selections by multiple domains from a potential game perspective. The equilibrium inefficiency in terms of price of stability is characterized under various conditions. In addition, we examine the well-established equilibrium selective learning algorithm B-logit and show that B-logit is a special case of a general family of algorithms, denoted by Γ collectively. A novel learning algorithm named MAX-logit is proposed, which retains the favorable equilibrium selection property with the provably fastest convergence speed than any other algorithms in Γ, and can be applied to many other applications of potential games. Simulation results show that MAX-logit can improve the convergence speed of B-logit by up to 33.85%.