KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction
Ching-Hao Fan, Hao Zhou, Yao Sun, G. Palomino-Roldán, Olga Kokshagina, Marc Santolini, Lijing Wang · 2025
Accurate user engagement prediction is critical to the success of crowdsourcing in online communities. This study focuses on analyzing user behavior in crowd-driven platforms, addressing the challenge of predicting engagement levels based on behavioral dynamics in online knowledge-sharing environments. We propose a comprehensive framework, Knowledge-Enhanced Graph Neural Networks (KEGNN), which begins by collecting and refining user activity data from a real-world platform and quantifying user engagement using the RFE (Recency, Frequency, Engagement) model to extract meaningful insights from the comprehensive record of the user's behavior. KEGNN leverages a graph neural network (GNN)-based architecture to capture temporal and spatial patterns in user dynamics, incorporating user-generated textual knowledge to enhance prediction performance. Extensive experiments on data from Just One Giant Lab (JOGL), an open knowledge-sharing platform, demonstrate that KEGNN outperforms adapted baseline models originally designed for other domains. The results highlight its effectiveness in predicting short- and long-term user engagement. Our framework provides a robust foundation for analyzing online user behavior and holds significant potential for adaptation to similar challenges across various domains. The source code is publicly available at: https://github.com/charliefanfan/ICMR-GNN