# Building a Graph-based Deep Learning network model from captured traffic traces

arXiv: https://arxiv.org/abs/2310.11889 · HTML: https://mlsanity.com/paper/2310.11889/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Computing Graph Neural Networks: A Survey from Algorithms to Accelerators](https://mlsanity.com/paper/2010.00130/) (2020)
- [RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1910.01508/) (2019)
- [Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities](https://mlsanity.com/paper/2112.14792/) (2021)
- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation](https://mlsanity.com/paper/2202.13956/) (2022)
- [Scalable Tail Latency Estimation for Data Center Networks](https://mlsanity.com/paper/2205.01234/) (2022)

## Built on by (most cited first, 2 of 2)

- [Improving the Real-Data Driven Network Evaluation Model for Digital Twin Networks](https://mlsanity.com/paper/2405.08473/) (2024)
- [From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction](https://mlsanity.com/paper/2512.20885/) (2025)

## Abstract (arXiv)

Currently the state of the art network models are based or depend on Discrete Event Simulation (DES). While DES is highly accurate, it is also computationally costly and cumbersome to parallelize, making it unpractical to simulate high performance networks. Additionally, simulated scenarios fail to capture all of the complexities present in real network scenarios. While there exists network models based on Machine Learning (ML) techniques to minimize these issues, these models are also trained with simulated data and hence vulnerable to the same pitfalls. Consequently, the Graph Neural Networking Challenge 2023 introduces a dataset of captured traffic traces that can be used to build a ML-based network model without these limitations. In this paper we propose a Graph Neural Network (GNN)-based solution specifically designed to better capture the complexities of real network scenarios. This is done through a novel encoding method to capture information from the sequence of captured packets, and an improved message passing algorithm to better represent the dependencies present in physical networks. We show that the proposed solution it is able to learn and generalize to unseen captured network scenarios.