# Distributed Scheduling using Graph Neural Networks

arXiv: https://arxiv.org/abs/2011.09430 · HTML: https://mlsanity.com/paper/2011.09430/

## Key facts

- Cited by 27 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 27 of 27)

- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Learning Resilient Radio Resource Management Policies with Graph Neural Networks](https://mlsanity.com/paper/2203.11012/) (2022)
- [Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks](https://mlsanity.com/paper/2104.05463/) (2021)
- [Power Allocation for Wireless Federated Learning using Graph Neural Networks](https://mlsanity.com/paper/2111.07480/) (2021)
- [HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing](https://mlsanity.com/paper/2110.05529/) (2021)
- [Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks](https://mlsanity.com/paper/2201.11799/) (2022)
- [Space-Time Graph Neural Networks with Stochastic Graph Perturbations](https://mlsanity.com/paper/2210.16270/) (2022)
- [Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks](https://mlsanity.com/paper/2203.14339/) (2022)
- [Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance Minimization](https://mlsanity.com/paper/2309.11856/) (2023)
- [Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search](https://mlsanity.com/paper/2108.01036/) (2021)
- [Delay-aware Backpressure Routing Using Graph Neural Networks](https://mlsanity.com/paper/2211.10748/) (2022)
- [Learning to Transmit with Provable Guarantees in Wireless Federated Learning](https://mlsanity.com/paper/2304.09329/) (2023)
- [Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling](https://mlsanity.com/paper/2105.09182/) (2021)
- [Distributed Optimization of Graph Convolutional Network using Subgraph Variance](https://mlsanity.com/paper/2110.02987/) (2021)
- [Stability Analysis of Unfolded WMMSE for Power Allocation](https://mlsanity.com/paper/2110.07471/) (2021)
- [Biased Backpressure Routing Using Link Features and Graph Neural Networks](https://mlsanity.com/paper/2407.09753/) (2024)
- [Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks](https://mlsanity.com/paper/2203.15030/) (2022)
- [Higher-order Sparse Convolutions in Graph Neural Networks](https://mlsanity.com/paper/2302.10505/) (2023)
- [Robust MIMO Detection using Hypernetworks with Learned Regularizers](https://mlsanity.com/paper/2110.07053/) (2021)
- [Graph Neural Network based scheduling : Improved throughput under a generalized interference model](https://mlsanity.com/paper/2111.00459/) (2021)
- [A Higher-Order Semantic Dependency Parser](https://mlsanity.com/paper/2201.11312/) (2022)
- [Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks](https://mlsanity.com/paper/2503.24203/) (2025)
- [Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)](https://mlsanity.com/paper/2509.05447/) (2025)
- [Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning](https://mlsanity.com/paper/2511.07428/) (2025)
- [When Does Learning Beat Heuristics? A Case Study in Kubernetes Scheduler Score Plugins](https://mlsanity.com/paper/2609.22142/) (2026)

## Abstract (arXiv)

A fundamental problem in the design of wireless networks is to efficiently schedule transmission in a distributed manner. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is NP-hard. For practical link scheduling schemes, distributed greedy approaches are commonly used to approximate the solution of the MWIS problem. However, these greedy schemes mostly ignore important topological information of the wireless networks. To overcome this limitation, we propose a distributed MWIS solver based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module learns topology-aware node embeddings that are combined with the network weights before calling a greedy solver. In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.