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

Graph structure learning | Papers With Code

Implementations of differentiable graph structure learning models are written in PyTorch, allowing us to leverage the rich software ecosystem that exists e. g., ...

Deep transformer-based heterogeneous spatiotemporal graph ...

Graph WaveNet: Graph WaveNet is a deep learning model that combines GCNs and gated recurrent units (GRUs) with the WaveNet architecture for geographical traffic ...

Graph-Structured Gaussian Processes for Transferable Graph ...

Transferable graph learning involves knowledge transferability from a source graph to a relevant target graph. The major challenge of transferable graph ...

Graph Neural Network for Traffic Forecasting: The Research Progress

Transfer learning has been proven effective for transferring cross-city knowledge, which will help address the cold-start problem in new cities [191].

Meta-Learning with Graph Neural Networks: Methods and Applications

There are two main challenges in applying meta-learning to graph-structured data. ... node classification problem on graphs by learning a transferable.

Spatio-Temporal Graph Structure Learning for Traffic Forecasting

The above concerns motivate us to propose Structure. Learning Convolution (SLC), a generic graph convolutional formulation which explicitly models the structure ...

Domain adversarial graph neural network with cross-city ... - CoLab

Specifically, DAGN comprises three key modules: (1) A cross-city graph structure learning module is developed to capture node-pair adjacent ...

AAGCN: a graph convolutional neural network with adaptive feature ...

Graph neural network methods within deep learning have shown remarkable capabilities in processing graph-structured data, such as social ...

Graph Neural Networks for Traffic Forecasting

GNNs are a class of deep learning methods that directly process the input as graph data. This leverages more directly the spatial dependencies ...

A binary-domain recurrent-like architecture-based dynamic graph ...

In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node ...

Forecasting Cellular Traffic by Leveraging Explicit Inductive ... - HAL

Yang, “Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities,” in Proceedings of the 29th ACM SIGKDD ...

Graph-to-Graph Transfer in Geometric Deep Learning

Our proposed project is a quantitative and qualitative study of graph-to-graph transfer in geometric deep learning in traffic data and code and methodologies ...

Graph Neural Network Series 3 — Focusing on the Details of Graphs

The introduction of GAT offers several advantages in the processing of graph-structured data. Firstly, GAT enhances the model's adaptability and ...

Graph Neural Network for Traffic Forecasting: The Research Progress

Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with ...

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

Previous work has identified diffusion convolutional recurrent neural networks, (DCRNN), as a state-of- the-art method for highway traffic forecasting. It ...

Graph autoencoder with mirror temporal convolutional networks for ...

Graph autoencoders (GAEs) are a kind of unsupervised learning method, which means they map nodes to a potential vector space through an encoding ...

american university of beirut transferability of graph neural networks ...

al was the first to investigate transfer learning on graph-structured data. ... Spatio-temporal graph convolutional networks: A deep learning ...

Temporal Graph Reading Group - McGill School Of Computer Science

Reihaneh Rabbany and Prof. Guillaume Rabusseau). He is interested in representation learning on temporal graphs, anomaly detection and graph representation ...

Graph Neural Networks, Part II: Graph Convolutional Networks - Sertis

This series of posts aims to talk about the concept and applications of graph neural networks (GNNs), which is a machine learning model ...

Graph Neural Networks for Intelligent Transportation Systems

stations and learning the graph structure instead of assuming a predefined structure. ... Also, graph learning, link prediction/estimation, transfer learning, ...