# Exploiting Social Graph Networks for Emotion Prediction

arXiv: https://arxiv.org/abs/2207.05820 · HTML: https://mlsanity.com/paper/2207.05820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation](https://mlsanity.com/paper/2008.09472/) (2020)
- [Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/2105.07752/) (2021)
- [A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data](https://mlsanity.com/paper/2201.06610/) (2022)

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

- [SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks](https://mlsanity.com/paper/2401.11113/) (2024)

## Abstract (arXiv)

Emotion prediction plays an essential role in mental health and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict happiness and stress. In addition to a person's physiological features, we also incorporate the environment's impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all the users. The construction of social networks does not incur additional cost in terms of EMAs or data collection from users and doesn't raise privacy concerns. We propose an architecture that automates the integration of a user's social network affect prediction, is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. Our extensive evaluation highlights the improvement provided by the integration of social networks. We further investigate the impact of graph topology on model's performance.