# TempODEGraphNet: predicting user churn using dynamic social graphs and neural ODEs

## TL;DR

This paper introduces a new model for predicting user churn in games by using dynamic social graphs and neural networks, showing better performance than static methods.

## Contribution

The novel contribution is a dynamic graph model using neural ODEs for improved user churn prediction in gaming.

## Key findings

- The proposed model achieves a higher F1 score compared to conventional algorithms and static graph models.
- Dynamic graphs more accurately reflect changes in user behavior in domains with active interactions like MMORPGs.

## Abstract

Research on user churn prediction has been conducted across various domains for a long time. Among these, the gaming domain is characterized by its potential for diverse types of interactions between users. Due to this characteristic, many studies on churn prediction have considered the relationships between users and have primarily applied social network analysis. Recently, the use of Graph Neural Networks (GNNs) has been actively applied. However, existing studies utilizing GNNs have limitations as they use static graphs that do not effectively capture the dynamic nature of interactions that change over time. This study addresses these limitations by proposing a dynamic graph model for predicting user churn in games based on user interactions. Data are sourced from 10,000 users of ’Blade & Soul’ by NCSOFT. The proposed model effectively captures changes in user behavior over time and predicts user churn with a focus on interactions among users. Experimental results reveal that the proposed model achieves a higher F1 score compared with conventional algorithms and static graph models. Dynamic graphs more accurately reflect changes in user behavior compared with static graphs, particularly in domains with active interactions such as massively multiplayer online role-playing games. This work highlights the significance of user churn prediction in the gaming industry and demonstrates the effectiveness of the predictive models that use dynamic graphs.