Implement, test, and organize recent reseach of GNN-based methods. Enable lifecycle controlled with MLflow.
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Updated
Jul 2, 2024 - Python
Implement, test, and organize recent reseach of GNN-based methods. Enable lifecycle controlled with MLflow.
Feature Expansion for Graph Neural Networks [ICML-2023]
Work we did for a practical course in graph learning, organized by department Informatik 7 at RWTH University
[ICML'24] BAT: 🚀 Boost Class-imbalanced Node Classification with <10 lines of Code | 从拓扑视角改善类别不平衡节点分类
repo for learning graph neural network
Pytorch implementation of Polarized message-passing graph neural networks published in Artificial Intelligence, 2024.
[IJCAI 2019] Source code and datasets for "Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification"
[Arxiv-2023] Official code for work "ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance"
Graph Equilibrium Networks: Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models
This repository contains the implementation of some of the popular Graph Neural Networks (GNNs) using PyTorch Geometric to solve node classification tasks.
Community-aware Graph Transformer (CGT) is a novel Graph Transformer model that utilizes community structures to address node degree biases in message-passing mechanism and developed by NS Lab @ CUK based on pure PyTorch backend.
This repository provides code for the paper "Forward Learning of Graph Neural Networks" (ICLR 2024).
This repository provides data splits for the paper "Forward Learning of Graph Neural Networks" (ICLR 2024).
Source code for EvalNE, a Python library for evaluating Network Embedding methods.
Node Classification on large Knowledge Graphs of Cora Dataset using Graph Neural Network (GNN) in Pytorch.
A repository of pretty cool datasets that I collected for network science and machine learning research.
The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"
Code for ECML-PKDD 2023 paper "Learning to Augment Graph Structure for both Homophily and Heterophily Graphs"
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
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