While social living is considered to be an indispensable part of human life in today's ever-connected world, social distancing has recently received much public attention on its importance since the outbreak of the coronavirus pandemic. In fact, social distancing has long been practiced in nature among solitary species, and been taken by human as an effective way of stopping or slowing down the spread of infectious diseases MESHD. Here we consider a social distancing problem for how a population, when in a world with a network of social sites, such as schools, restaurants, shopping centers, residential areas, etc., decides to visit or stay at some sites while avoiding or closing down others so that the social contacts across the network can be minimized. We model this problem as a network population game, where every individual tries to find some network sites to visit or stay so that he/she can minimize all his/her contacts. In the end, an optimal strategy can be found for every one, when the resulting distribution of the population over the network reaches an equilibrium. We show that a large class of equilibrium strategies can be obtained by selecting a set of network sites that forms a so-called maximal r-regular subnetwork. The latter includes many well studied network types, such as the maximal independent set (r=0), the maximal strong matching (r=1), the maximal set of independent cycles (r=2), etc. They are easy to identify or construct, and can be completely disconnected (with r = 0) for the most strict isolation, or allow certain degree of connectivities (with r > 0) for more flexible distancing. We derive the equilibrium conditions of these strategies, and analyze their rigidity HP and flexibility on different types of r-regular subnetworks. We provide an overview on algorithms that can be used for computing maximal r-regular subnetworks and their associated distancing strategies.