This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric
-
Updated
May 30, 2024 - Python
This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric
PyTorch implementation of GNN models
Molecular substructure graph attention network for molecular property identification in drug discovery. This is the starting point for my thesis project and is the fork of a repository from the paper https://doi.org/10.1016/j.patcog.2022.108659
S&P100 stocks analysis via Graph Neural Networks (Forecasting, Clustering, Trend classification, Stocks ranking for optimal stock picking)
This project is a scalable unified framework for deep graph clustering.
[ICDE2023] A PyTorch implementation of Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics Framework (START).
Using to predict the highway traffic speed
[ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
Differentiable clustering for graph attention-TKDE 2024
Developing efficient classification for Reddit posts/comments/communities with Graph Neural Networks (GNNs)
A Novel Spatio-Temporal Generative Inference Network for Predicting the Long-Term Highway Traffic Speed
PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).
An Explainable Geometric-Weighted Graph Attention Network (xGW-GAT) for Identifying Functional Networks Associated with Gait Impairment
Anti Money Laundering Detection using Graph Attention Network
learning station embedding
Official implementation for "Tailoring Self-Attention for Graph via Rooted Subtrees" (NeurIPS2023)
Official implementation for "Tailoring Self-Attention for Graph via Rooted Subtrees" (NeurIPS2023)
An enhanced model known as RAGATv2 which is built upon the structure of the Relation Aware Graph Attention Network (RAGAT)
A collection of projects using graph neural networks implemented from first principles, and using the PyTorch Geometric library
Official repository for On Over-Squashing in Message Passing Neural Networks (ICML 2023)
Add a description, image, and links to the graph-attention-networks topic page so that developers can more easily learn about it.
To associate your repository with the graph-attention-networks topic, visit your repo's landing page and select "manage topics."