An enhanced model known as RAGATv2 which is built upon the structure of the Relation Aware Graph Attention Network (RAGAT)
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Updated
Sep 30, 2023 - Jupyter Notebook
An enhanced model known as RAGATv2 which is built upon the structure of the Relation Aware Graph Attention Network (RAGAT)
Implement, test, and organize recent reseach of GNN-based methods. Enable lifecycle controlled with MLflow.
Bachelor Thesis
Empirical Research over the possible advantages of pretraining a Graph Neural Network for Classification by using Link Prediction. We used GCN, GAT and GraphSAGE with minibatch generation. Done for the Learning From Networks course taught by professor Fabio Vandin at the University of Padova
Asynchronous lending iterator
This repository contains the implementation of some of the popular Graph Neural Networks (GNNs) using PyTorch Geometric to solve node classification tasks.
Ziyuan Chen & Zhirong Chen, 2022 Summer Research @ ZJU
An attempt to apply graph attention neural network on modelling EU electricity interconnectors with only public data.
🎩 First experience with Gatsby & the JAM stack
Zero-to-hero for Graph Neural Networks
Graph Attention Networks (GATs)
Dense and Sparse Implementation of GAT written by PyTorch
Developing efficient classification for Reddit posts/comments/communities with Graph Neural Networks (GNNs)
This repository presents and compares HeterSUMGraph and variants using GATConv, GATv2Conv and a combination of HeterSUMGraph and SummaRuNNer (using HeterSUMGraph as a sentence encoder).
Graph4CTR(GCNs, GATs, HGCNs)
Reproducible generative learning
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