# Spatio-Temporal Hybrid Graph Convolutional Network for Traffic Forecasting in Telecommunication Networks

arXiv: https://arxiv.org/abs/2009.09849 · HTML: https://mlsanity.com/paper/2009.09849/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [3D-IDS: Doubly Disentangled Dynamic Intrusion Detection](https://mlsanity.com/paper/2307.11079/) (2023)
- [Solarcast-ML: Per Node GraphCast Extension for Solar Energy Production](https://mlsanity.com/paper/2406.13559/) (2024)
- [Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks](https://mlsanity.com/paper/2112.12321/) (2021)
- [Traffic Prediction in Cellular Networks using Graph Neural Networks](https://mlsanity.com/paper/2301.12605/) (2023)
- [A Generic Approach to Integrating Time into Spatial-Temporal Forecasting via Conditional Neural Fields](https://mlsanity.com/paper/2305.06827/) (2023)
- [Deep Learning-driven Mobile Traffic Measurement Collection and Analysis](https://mlsanity.com/paper/2410.19777/) (2024)
- [A Pretrained Probabilistic Transformer for City-Scale Traffic Volume Prediction](https://mlsanity.com/paper/2506.02654/) (2025)

## Abstract (arXiv)

Telecommunication networks play a critical role in modern society. With the arrival of 5G networks, these systems are becoming even more diversified, integrated, and intelligent. Traffic forecasting is one of the key components in such a system, however, it is particularly challenging due to the complex spatial-temporal dependency. In this work, we consider this problem from the aspect of a cellular network and the interactions among its base stations. We thoroughly investigate the characteristics of cellular network traffic and shed light on the dependency complexities based on data collected from a densely populated metropolis area. Specifically, we observe that the traffic shows both dynamic and static spatial dependencies as well as diverse cyclic temporal patterns. To address these complexities, we propose an effective deep-learning-based approach, namely, Spatio-Temporal Hybrid Graph Convolutional Network (STHGCN). It employs GRUs to model the temporal dependency, while capturing the complex spatial dependency through a hybrid-GCN from three perspectives: spatial proximity, functional similarity, and recent trend similarity. We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks. Our experimental results demonstrate the superiority of the proposed model in that it consistently outperforms both classical methods and state-of-the-art deep learning models, while being more robust and stable.