# GDDR: GNN-based Data-Driven Routing

arXiv: https://arxiv.org/abs/2104.09919 · HTML: https://mlsanity.com/paper/2104.09919/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1901.08113/) (2019)
- [Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning](https://mlsanity.com/paper/1906.08879/) (2019)
- [A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization](https://mlsanity.com/paper/1709.07080/) (2017)
- [Hierarchical Deep Double Q-Routing](https://mlsanity.com/paper/1910.04041/) (2019)
- [Learning Index Selection with Structured Action Spaces](https://mlsanity.com/paper/1909.07440/) (2019)

## Built on by (most cited first, 9 of 9)

- [Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective](https://mlsanity.com/paper/2404.06492/) (2024)
- [Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing](https://mlsanity.com/paper/2402.05027/) (2024)
- [A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks](https://mlsanity.com/paper/2406.13125/) (2024)
- [Graph Neural Modeling of Network Flows](https://mlsanity.com/paper/2209.05208/) (2022)
- [Deal: Distributed End-to-End GNN Inference for All Nodes](https://mlsanity.com/paper/2503.02960/) (2025)
- [Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks](https://mlsanity.com/paper/2509.04973/) (2025)
- [TrajAware: Graph Cross-Attention and Trajectory-Aware for Generalisable VANETs under Partial Observations](https://mlsanity.com/paper/2509.06665/) (2025)
- [Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning](https://mlsanity.com/paper/2409.00754/) (2024)
- [Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies](https://mlsanity.com/paper/2409.07932/) (2024)

## Abstract (arXiv)

We explore the feasibility of combining Graph Neural Network-based policy architectures with Deep Reinforcement Learning as an approach to problems in systems. This fits particularly well with operations on networks, which naturally take the form of graphs. As a case study, we take the idea of data-driven routing in intradomain traffic engineering, whereby the routing of data in a network can be managed taking into account the data itself. The particular subproblem which we examine is minimising link congestion in networks using knowledge of historic traffic flows. We show through experiments that an approach using Graph Neural Networks (GNNs) performs at least as well as previous work using Multilayer Perceptron architectures. GNNs have the added benefit that they allow for the generalisation of trained agents to different network topologies with no extra work. Furthermore, we believe that this technique is applicable to a far wider selection of problems in systems research.