<p>This study examines key properties of tourist destination networks. We constructed a network data set using a large collection of tourist trip data that contains 217,686 distinct trips taken by 200,153 tourists across 39,901 cities in 195 countries. Using the data set, we mapped and simulated five global tourist destination networks based on three canonical network models. We found that all five tourist destination networks exhibit high clustering and scale-free properties, which are consistent with other social and economic networks, but have much longer diameters, not observed in social and economic networks. We found that none of the three network models could adequately capture the formation mechanisms of the tourist destination networks, which only share certain key properties with each of the three network models separately. This inadequacy calls for the development of new network models to examine the connectivity and topologies of tourist destinations that are grounded in tourists’ decision-making.</p>