Sleep behavior significantly impacts health and acts as an indicator of physical and mental well-being. Monitoring and predicting sleep behavior with ubiquitous sensors may therefore assist in both sleep management and tracking of related health conditions. While sleep behavior depends on, and is reflected in the physiology of a person, it is also impacted by external factors such as digital media usage, social network contagion, and the surrounding weather. In this work, we propose SPAND (Sleep Prediction Architecture using Network Dynamics), a system that exploits social contagion in sleep behavior through graph networks and integrates it with physiological and phone data extracted from ubiquitous mobile and wearable devices for predicting next-day sleep labels about sleep duration. Our architecture overcomes the limitations of large-scale graphs containing connections irrelevant to sleep behavior by devising an attention mechanism. The extensive experimental evaluation highlights the improvement provided by incorporating social networks in the model. Additionally, we conduct robustness analysis to demonstrate the system's performance in real-life conditions. The outcomes affirm the stability of SPAND against perturbations in input data. Further analyses emphasize the significance of network topology in prediction performance revealing that users with higher eigenvalue centrality are more vulnerable to data perturbations.

通过利用社交传染性和图网络，将人的生理学、电话数据与手机和可穿戴设备中提取的生理学和电话数据融合在一起，我们提出了SPAND（使用网络动态的睡眠预测架构），用于预测关于睡眠持续时间的次日睡眠标签，结果证明了社交网络在模型中的改进和网络拓扑对预测性能的重要性。

SPAND: 使用网络动态的睡眠预测架构