# Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation

arXiv: https://arxiv.org/abs/2505.07674 · HTML: https://mlsanity.com/paper/2505.07674/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 12 May 2025.

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

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.14992/) (2020)
- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining](https://mlsanity.com/paper/2412.15593/) (2024)
- [A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention](https://mlsanity.com/paper/2504.15223/) (2025)
- [Dynamic Scheduling Strategies for Resource Optimization in Computing Environments](https://mlsanity.com/paper/2412.17301/) (2024)
- [Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning](https://mlsanity.com/paper/2504.19583/) (2025)
- [DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning](https://mlsanity.com/paper/2310.11777/) (2023)

## Built on by (most cited first, 6 of 6)

- [Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters](https://mlsanity.com/paper/2508.10253/) (2025)
- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment](https://mlsanity.com/paper/2508.09399/) (2025)
- [Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modeling](https://mlsanity.com/paper/2508.10255/) (2025)
- [Cross-Modal Deep Metric Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2509.12540/) (2025)

## Abstract (arXiv)

This study focuses on the challenge of predicting network traffic within complex topological environments. It introduces a spatiotemporal modeling approach that integrates Graph Convolutional Networks (GCN) with Gated Recurrent Units (GRU). The GCN component captures spatial dependencies among network nodes, while the GRU component models the temporal evolution of traffic data. This combination allows for precise forecasting of future traffic patterns. The effectiveness of the proposed model is validated through comprehensive experiments on the real-world Abilene network traffic dataset. The model is benchmarked against several popular deep learning methods. Furthermore, a set of ablation experiments is conducted to examine the influence of various components on performance, including changes in the number of graph convolution layers, different temporal modeling strategies, and methods for constructing the adjacency matrix. Results indicate that the proposed approach achieves superior performance across multiple metrics, demonstrating robust stability and strong generalization capabilities in complex network traffic forecasting scenarios.