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benchmarking-gnns-pyg

chemprop

Message Passing Neural Networks for Molecule Property Prediction

cohesive_subgraph_book

comp47027703data7703

deepsem

deepsnap

Python library assists deep learning on graphs

detr

End-to-End Object Detection with Transformers

gat

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

gnnpapers

Must-read papers on graph neural networks (GNN)

graph-transformer-pytorch

Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

graphgym

Platform for designing and evaluating Graph Neural Networks (GNN)

graphormer

Graphormer is a deep learning package that allows researchers and developers to train custom models for molecule modeling tasks. It aims to accelerate the research and application in AI for molecule science, such as material design, drug discovery, etc.

guangxinsuuu.github.io

hetehg-vae

Heterogeneous Hypergraph Variational Autoencoder for Link Prediction (T-PAMI 2021)

hyperg-vae

Here we present HyperG-VAE, a Bayesian deep generative model that utilizes a hypergraph for analyzing single-cell RNA sequencing (scRNA-seq) data and constructs reliable GRNs.

m-tree

A data structure for efficient nearest-neighbor queries.

magnn

Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

ml

The project in the way of my ML learn

modular_tree

molclr

Implementation of "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyTorch Geometric

nnpulearning

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

positive-and-unlabeled-learning-from-imbalanced-data

Code for the paper named "Positive-Unlabeled Learning from Imbalanced Data" which has been accepted by IJCAI-21

pygat

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

pygcn

Graph Convolutional Networks in PyTorch

pytorchgeometrictutorial

Pytorch Geometric Tutorials

self-pu

[ICML2020] "Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training" by Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang

simclr

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations