Online game player churn prediction based on multiplex social influence through network embedding

Accurately predicting player churn is crucial for designing effective retention strategies and driving revenue in the online gaming industry. While existing predictive models achieve reasonable performance relying on individual demographic and behavioral data, they largely overlook the multiplex social influence arising from diverse, overlapping player relationships. To address this gap, we propose a novel Multiplex Social Influence (MSI) framework for player churn prediction. Specifically, we construct multiplex social networks encompassing both explicit friendship ties and implicit Player-versus-Environment (PvE) teammate relationships. We then apply a network embedding algorithm called MLNRL to efficiently extract multi-scale structural information from these distinct networks. These embeddings are concatenated to form a unified multiplex social influence representation and combined with players' behavioral attributes for downstream classification. Extensive experiments on a real-world online game dataset validate the superiority of our proposed approach. Quantitatively, the MSI framework paired with an SVM (RBF kernel) classifier achieves outstanding prediction performance (accuracy: 0.93, F1-score: 0.93, AUC: 0.98. Furthermore, rigorous ablation studies, comprehensive robustness tests (covering network embedding algorithms, fusion strategies, and data imbalance issues), and parameter sensitivity analyses collectively confirm that integrating multiplex social influence yields robust synergistic effects, significantly outperforming baseline models built on single relationship types. These findings definitively confirm the effectiveness and necessity of incorporating multiplex social structures into data-driven churn prediction models for online game services.