GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

• URL: http://arxiv.org/abs/2607.19270v1
• Date: Tue, 21 Jul 2026 16:41:59 GMT
• Title: GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
• Abstract summary: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.<n>This research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding.<n>This framework standardizes the input space regardless of network scale.<n>It provides a versatile blueprint for generalized origin-destination spatial problems, such as freight logistics and multimodal network optimization.
• Score: 46.28228608289378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap. Standard models rely on transductive feature initializations that tie travel demand to fixed network topologies, preventing seamless transfer to new urban environments. To overcome this structural limitation, this research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding (GUIDED). By injecting travel demand as a scalar attribute on auxiliary virtual links rather than as specific node features, this modular framework standardizes the input space regardless of network scale. Extensive experimental evaluation across multiple urban topologies demonstrates that a Heterogeneous Graph Attention Network (HetGAT) model integrated with the proposed GUIDED layer maintains state-of-the-art predictive accuracy on single-network tasks, while demonstrating superior robustness to out-of-distribution demand patterns and maintaining a distinct performance advantage over the baseline even under severe data scarcity. Notably, the proposed feature initialization enables highly parameter-efficient domain adaptation for inter-network transfer learning without artificial input homogenization, establishing a robust foundation for truly inductive models. At the same time, the optimized scatter operations of the initialization layer yield an approximate 50% reduction in training time per epoch compared to the baseline approach. Furthermore, while demonstrated on vehicular traffic, this fundamental abstraction of spatial topology provides a versatile blueprint for generalized origin-destination spatial problems, such as freight logistics and multimodal network optimization.

Related papers

• A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting [27.679556042019076]
  Existing pre-trained models often rely on a homogeneous modeling paradigm to handle highly heterogeneous traffic data.<n>We propose FlexST, a novel pre-training framework that introduces modularity and adaptivity for traffic modeling.<n>Experiments on 23 real-world traffic datasets demonstrate that FlexST significantly outperforms state-of-the-art baselines in zero- and few-shot settings.
  arXiv  Detail & Related papers  (2026-09-12T11:04:04Z)
• Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction [10.55782327097476]
  This paper proposes the Structure-Guided Stemporal Attention Graph Neural Network (SGSAN) for traffic prediction.<n>We show that SGSAN achieves state-of-the-art predictive accuracy while providing built-in interpretability that organically aligns with traffic network logic.
  arXiv  Detail & Related papers  (2026-08-14T10:46:37Z)
• Position: Weight Space Should Be a First-Class Generative AI Modality [67.10702320971893]
  This paper argues that model checkpoints should be treated as a first-class data modality.<n>It argues that generative modeling in weight space should be standardized as a core machine learning primitive.<n>Our goal is to shift the community's default mindset from optimizing models per task to sampling models from learned weight distributions.
  arXiv  Detail & Related papers  (2026-05-18T16:38:26Z)
• A Graph Foundation Model for Wireless Resource Allocation [37.503398874234094]
  We propose a graph foundation model for resource allocation based on a pre-training and fine-tuning paradigm to extract unified representations.<n>Specifically, we introduce an interference-aware Transformer architecture with a bias projector that injects interference topologies into global attention mechanisms.<n>Experiments demonstrate that the proposed framework achieves state-of-the-art performance and scales effectively with increased model capacity.
  arXiv  Detail & Related papers  (2026-04-08T06:07:28Z)
• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• Constraints Matrix Diffusion based Generative Neural Solver for Vehicle Routing Problems [16.541111954252397]
  We propose a novel fusion neural network framework that employs a discrete noise graph diffusion model to learn the underlying constraints of vehicle routing problems.<n>This work represents the first comprehensive experimental investigation of neural network model solvers across a 378-combinatorial space.
  arXiv  Detail & Related papers  (2026-03-08T10:03:34Z)
• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models [0.0]
  This work introduces an interpretability enhancement procedure for graph neural networks (GNNs) The end result is an interpretable GNN model that isolates regions in physical space, corresponding to sub-graphs, that are intrinsically linked to the forecasting task. The interpretable GNNs can also be used to identify, during inference, graph nodes that correspond to a majority of the anticipated forecasting error.
  arXiv  Detail & Related papers  (2023-11-13T18:37:07Z)
• End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment [5.205252810216621]
  We leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment. Our model integrates an adaptive graph attention mechanism with auxiliary "virtual" links connecting origin-destination node pairs. We show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy.
  arXiv  Detail & Related papers  (2023-10-19T23:04:09Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks [119.81868223344173]
  This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
  arXiv  Detail & Related papers  (2020-06-26T14:20:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.