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

Distributed Node Covering Optimization for Large Scale Networks and Its Application on Social Advertising