  Combinatorial optimizations are usually complex and inefficient, which limits
their applications in large-scale networks with billions of links. We introduce
a distributed computational method for solving a node-covering problem at the
scale of factual scenarios. We first construct a genetic algorithm and then
design a two-step strategy to initialize the candidate solutions. All the
computational operations are designed and developed in a distributed form on
\textit{Apache Spark} enabling fast calculation for practical graphs. We apply
our method to social advertising of recalling back churn users in online mobile
games, which was previously only treated as a traditional item recommending or
ranking problem.
