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

TL;DR

This paper presents a distributed genetic algorithm approach on Apache Spark for large-scale node-covering problems, demonstrated through social advertising in mobile gaming to efficiently recall churn users.

Contribution

It introduces a scalable distributed method combining genetic algorithms and a two-step initialization strategy for large-scale node covering problems.

Findings

Enables fast computation on large graphs using Apache Spark.

Effectively applies to social advertising for churn user recall.

Improves efficiency over traditional methods.

Abstract

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.