Transferable Graph Structure Learning for Graph|based Traffic ...

Transferable Graph Structure Learning for Graph-based Traffic ...

We propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and transfers the graph structures and forecasting ...

Transferable Graph Structure Learning for Graph-based Traffic ...

ABSTRACT. Graph-based deep learning models are powerful in modeling spatio- temporal graphs for traffic forecasting.

Transferable Graph Structure Learning for Graph-based Traffic ...

Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities ... Graph-based deep learning models are powerful in modeling ...

Transferable Graph Structure Learning for Graph-based Traffic ...

ABSTRACT. Graph-based deep learning models are powerful in modeling spatio- temporal graphs for traffic forecasting.

Transferable Graph Structure Learning for Graph-based Traffic ...

Graph-based deep learning models are powerful in modeling spatio-temporal graphs for traffic forecasting. In practice, accurate forecasting models rely on ...

KDD 2023 - Transferable Graph Structure Learning for ... - YouTube

... Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities. Graph-based Traffic Forecasting is a fundamental ...

Y. Jin, K. Chen, and Q. Yang, "Transferable graph structure learning ...

Chen, and Q. Yang, "Transferable graph structure learning for graph-based traffic forecasting across cities," in Proceedings of the 29th ACM SIGKDD ...

Transferable Graph Structure Learning for Graph-based Traffic ...

In this study, max flow analysis processes are carried out with a graph theory-based approach that can be used in optimizing the traffic load in transportation ...

awesome-graph-structure-learning/README.md at main - GitHub

2023 · [ICDE 2023] Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting [Paper] · [CIKM 2023] RDGSL: Dynamic Graph Representation Learning with ...

arXiv:2406.02614v2 [cs.LG] 6 Jun 2024

... Transferable graph structure learning for graph-based traffic fore- casting across cities. In: Proceedings of the 29th ACM SIGKDD Conference ...

Transferable traffic signal control: Reinforcement learning with graph ...

The key idea consists of two parts; 1) to represent the traffic state as graph-structured data by embedding it into a graph, 2) to process the information- ...

Graph-Structured Gaussian Processes for Transferable Graph ...

The major challenge of transferable graph learning is the distribution shift between source and target graphs induced by individual node attributes and complex ...

Transfer learning based graph convolutional network with self ...

To tackle these challenges, in this paper, we propose a transfer learning based graph convolutional network with self-attention mechanism method to detect ...

A curated list of papers on graph transfer learning (GTL). - GitHub

This repository contains a curated list of papers on graph transfer learning (GTL), which are categorized based on their published years.

Prompt-Based Spatio-Temporal Graph Transfer Learning - arXiv

Although transfer learning has been proposed to remedy this problem by leveraging knowledge across domains, the cross-task generalization still ...

Graph Neural Network Transfer Learning | Restackio

Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph-structured data across various domains, including social ...

NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction

A novel transfer learning approach to solve the trafﬁc prediction with few data, which can transfer the knowledge learned from a data-rich source domain to ...

Transfer Learning with Graph Neural Networks for Short-Term ...

For generalized, non-Euclidean data, graph convolutional neural network (GCNN) (Henaff, Bruna, and Le-Cun 2015) have been used for traffic network modeling and ...

Transferability of Graph Neural Networks using Graphon ... - YouTube

become powerful tools for processing graph-based ... Learning in the presence of low-dimensional structure: a spiked random matrix perspective – ...

[PDF] Transfer Learning with Graph Neural Networks for Short-Term ...

TL-DCRNN is developed, a new transfer learning approach for DCRNN, where a single model trained on a highway network can be used to forecast traffic on ...