PyTorch implementation of GNN models
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Updated
Jul 6, 2024 - Jupyter Notebook
PyTorch implementation of GNN models
A Novel Spatio-Temporal Generative Inference Network for Predicting the Long-Term Highway Traffic Speed
This project is a scalable unified framework for deep graph clustering.
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
Master thesis: JAT (Jraph Attention Networks), a deep learning architecture to predict the potential energy and forces of molecules. Adapts Graph Attention Networks (GATv2) within the Message Passing Neural Networks framework to computational chemistry in JAX
Source code for the GAtt method in "Revisiting Attention Weights as Interpretations of Message-Passing Neural Networks".
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
S&P100 stocks analysis via Graph Neural Networks (Forecasting, Clustering, Trend classification, Stocks ranking for optimal stock picking)
This repository contains the implementation of some of the popular Graph Neural Networks (GNNs) using PyTorch Geometric to solve node classification tasks.
[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)
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)
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