# SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks

arXiv: https://arxiv.org/abs/2401.11113 · HTML: https://mlsanity.com/paper/2401.11113/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 79 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Does Batch Normalization Help Optimization?](https://mlsanity.com/paper/1805.11604/) (2018)
- [Ensembles of Deep LSTM Learners for Activity Recognition using Wearables](https://mlsanity.com/paper/1703.09370/) (2017)
- [Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks](https://mlsanity.com/paper/1610.01683/) (2016)
- [Impact of Physical Activity on Sleep:A Deep Learning Based Exploration](https://mlsanity.com/paper/1607.07034/) (2016)
- [The power of dynamic social networks to predict individuals' mental health](https://mlsanity.com/paper/1908.02614/) (2019)
- [Exploiting Social Graph Networks for Emotion Prediction](https://mlsanity.com/paper/2207.05820/) (2022)

## Built on by (most cited first, 1 of 1)

- [GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments](https://mlsanity.com/paper/2506.10120/) (2025)

## Abstract (arXiv)

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 SleepNet, 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 SleepNet 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.