# Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

arXiv: https://arxiv.org/abs/2303.00524 · HTML: https://mlsanity.com/paper/2303.00524/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 92 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 30 of 32)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs](https://mlsanity.com/paper/2010.05337/) (2020)
- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [HyGCN: A GCN Accelerator with Hybrid Architecture](https://mlsanity.com/paper/2001.02514/) (2020)
- [Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/1911.12093/) (2019)
- [Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks](https://mlsanity.com/paper/1903.10527/) (2019)
- [Coupled Layer-wise Graph Convolution for Transportation Demand Prediction](https://mlsanity.com/paper/2012.08080/) (2020)
- [Federated Graph Neural Networks: Overview, Techniques and Challenges](https://mlsanity.com/paper/2202.07256/) (2022)
- [GRIP: A Graph Neural Network Accelerator Architecture](https://mlsanity.com/paper/2007.13828/) (2020)
- [Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction](https://mlsanity.com/paper/1905.06335/) (2019)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach](https://mlsanity.com/paper/1706.06279/) (2017)
- [Central Server Free Federated Learning over Single-sided Trust Social Networks](https://mlsanity.com/paper/1910.04956/) (2019)
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [Unveiling the potential of Graph Neural Networks for robust Intrusion Detection](https://mlsanity.com/paper/2107.14756/) (2021)
- [3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting](https://mlsanity.com/paper/1903.00919/) (2019)
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction](https://mlsanity.com/paper/2009.00096/) (2020)
- [Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts](https://mlsanity.com/paper/1902.06515/) (2019)
- [p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks](https://mlsanity.com/paper/2111.14837/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis](https://mlsanity.com/paper/2606.23010/) (2026)
- [Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends](https://mlsanity.com/paper/2401.00713/) (2024)
- [Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction](https://mlsanity.com/paper/2412.03188/) (2024)
- [An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms](https://mlsanity.com/paper/2509.10507/) (2025)
- [Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNs](https://mlsanity.com/paper/2512.17352/) (2025)

## Abstract (arXiv)

Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promising for this application. This approach models city regions as nodes in a transportation graph and their relations as edges. GNNs utilize local node features and the graph structure in the prediction. However, more efficient forecasting can still be achieved by following two main routes; enlarging the scale of the transportation graph, and simultaneously exploiting different types of nodes and edges in the graphs. However, both approaches are challenged by the scalability of GNNs. An immediate remedy to the scalability challenge is to decentralize the GNN operation. However, this creates excessive node-to-node communication. In this paper, we first characterize the excessive communication needs for the decentralized GNN approach. Then, we propose a semi-decentralized approach utilizing multiple cloudlets, moderately sized storage and computation devices, that can be integrated with the cellular base stations. This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization. Also, we propose a heterogeneous GNN-LSTM algorithm for improved taxi-level demand and supply forecasting for handling dynamic taxi graphs where nodes are taxis. Extensive experiments over real data show the advantage of the semi-decentralized approach as tested over our heterogeneous GNN-LSTM algorithm. Also, the proposed semi-decentralized GNN approach is shown to reduce the overall inference time by about an order of magnitude compared to centralized and decentralized inference schemes.