---
title: "GUIDED: Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.19270v1"
permalink: "https://feed.astralor.com/items/itm_dd13b0bf839e7bf9be0d7ff9"
published: "2026-07-21T16:41:59.000Z"
score: 78
channel: "ai"
tags: ["GUIDED","HetGAT","Traffic Assignment Problem","Graph Neural Network"]
---

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

## AI commentary

> This paper directly tackles the core bottleneck of generalizing GNNs from single-city to multi-city scenarios, with the proposed GUIDED layer being both simple and effective. It is especially important for intelligent transportation planners and deep learning researchers, offering a plug-and-play feature initialization that may inspire spatial transfer learning in other graph-based problems.

## At a glance

To address the spatial generalization gap of Graph Neural Networks in traffic assignment, this paper proposes a network-agnostic initialization layer called GUIDED, which injects travel demand as a scalar attribute on auxiliary virtual links to standardize input space across arbitrary network topologies. Experiments show that the HetGAT model with GUIDED maintains state-of-the-art single-network accuracy, is robust to out-of-distribution demand and data scarcity, enables parameter-efficient cross-network transfer learning, and reduces training time per epoch by approximately 50%.

## Key points

- Proposes GUIDED initialization layer that injects demand via auxiliary virtual links for network-agnostic feature initialization
- HetGAT with GUIDED maintains high accuracy across multiple urban topologies, robust to out-of-distribution demand and data scarcity
- Enables parameter-efficient cross-network transfer learning without artificial input homogenization, reducing training time per epoch by ~50%
- Approach generalizes to origin-destination spatial problems like freight logistics and multimodal network optimization

## Full text

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.

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