mlsanityData to 14 Oct 2026

GDDR: GNN-based Data-Driven Routing

arXiv 2104.09919 ↗First posted 20 Apr 2021cs.LG cs.NIcited by 9

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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.

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

10 of 38 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  4. 2016OpenAI Gymcited by 1,736
  5. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  6. 2019Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDNcited by 27
  7. 2019Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learningcited by 26
  8. 2017A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimizationcited by 14
  9. 2019Hierarchical Deep Double Q-Routingcited by 6
  10. 2019Learning Index Selection with Structured Action Spacescited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 5; 2025: 3.

9 papers.

  1. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspectivecited by 16
  2. 2024Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passingcited by 8
  3. 2024A Unified Framework for Combinatorial Optimization Based on Graph Neural Networkscited by 5
  4. 2022Graph Neural Modeling of Network Flowscited by 3
  5. 2025Deal: Distributed End-to-End GNN Inference for All Nodescited by 1
  6. 2025Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networkscited by 1
  7. 2025TrajAware: Graph Cross-Attention and Trajectory-Aware for Generalisable VANETs under Partial Observationscited by 1
  8. 2024Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learningcited by 0
  9. 2024Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategiescited by 0