# Temporal social network modeling of mobile connectivity data with graph neural networks

arXiv: https://arxiv.org/abs/2509.03319 · HTML: https://mlsanity.com/paper/2509.03319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018)
- [Towards Better Evaluation for Dynamic Link Prediction](https://mlsanity.com/paper/2207.10128/) (2022)
- [Variational Graph Recurrent Neural Networks](https://mlsanity.com/paper/1908.09710/) (2019)
- [Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities](https://mlsanity.com/paper/2302.01018/) (2023)
- [Graph Neural Networks: Taxonomy, Advances and Trends](https://mlsanity.com/paper/2012.08752/) (2020)
- [Temporal motifs reveal homophily, gender-specific patterns and group talk in mobile communication networks](https://mlsanity.com/paper/1302.2563/) (2013)
- [Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic](https://mlsanity.com/paper/2108.08307/) (2021)
- [A survey of results on mobile phone datasets analysis](https://mlsanity.com/paper/1502.03406/) (2015)
- [Bursty Human Dynamics](https://mlsanity.com/paper/1803.02580/) (2018)
- [Calling Dunbar's Numbers](https://mlsanity.com/paper/1604.02400/) (2016)
- [Social Network Differences of Chronotypes Identified from Mobile Phone Data](https://mlsanity.com/paper/1709.06690/) (2017)
- [Sex differences in social focus across the lifecycle in humans](https://mlsanity.com/paper/1508.06878/) (2015)
- [Communication with family and friends across the life course](https://mlsanity.com/paper/1512.09114/) (2015)
- [Social Physics: Uncovering Human Behaviour from Communication](https://mlsanity.com/paper/1804.04907/) (2018)
- [Different patterns of social closeness observed in mobile phone communication](https://mlsanity.com/paper/1808.10166/) (2018)
- [Differences of communication activity and mobility patterns between urban and rural people](https://mlsanity.com/paper/2311.13652/) (2023)

## Abstract (arXiv)

Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people's mobile connectivity data has not been extensively investigated. In the present study, we investigate four snapshot - based temporal GNNs in predicting the phone call and SMS activity between users of a mobile communication network. In addition, we develop a simple non - GNN baseline model using recently proposed EdgeBank method. Our analysis shows that the ROLAND temporal GNN outperforms the baseline model in most cases, whereas the other three GNNs perform on average worse than the baseline. The results show that GNN based approaches hold promise in the analysis of temporal social networks through mobile connectivity data. However, due to the relatively small performance margin between ROLAND and the baseline model, further research is required on specialized GNN architectures for temporal social network analysis.