Optimization of multiple sampling for solving network boundary specification problem

Missing data caused by boundary specification has a detrimental effect on the analysis of network structures, and designing optimal sampling methods is crucial for conducting network investigations. The present study discusses the boundary specification problem in multiple surveys, and proposes a mathematical model for optimizing the sampling strategy in each independent survey. A memetic algorithm that maximizes the sample representativeness is proposed as well, and experiments have proved the effectiveness and efficiency of the proposed algorithm. Zachary's Karate Club network and three networks of migrant workers are also performed to explain the social meaning of the optimal sampling method.