gnn
There are 648 repositories under gnn topic.
thunlp/GNNPapers
Must-read papers on graph neural networks (GNN)
benedekrozemberczki/pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
FighterLYL/GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
tensorflow/gnn
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
alibaba/graph-learn
An Industrial Graph Neural Network Framework
benedekrozemberczki/CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
gunrock/gunrock
Programmable CUDA/C++ GPU Graph Analytics
tsinghua-fib-lab/GNN-Recommender-Systems
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
shenweichen/GraphNeuralNetwork
Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
benedekrozemberczki/SimGNN
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
benedekrozemberczki/datasets
A repository of pretty cool datasets that I collected for network science and machine learning research.
joeat1/GNN_note
图神经网络整理
chaitjo/efficient-gnns
Code and resources on scalable and efficient Graph Neural Networks
hazdzz/STGCN
The PyTorch implementation of STGCN.
alibaba/libgrape-lite
🍇 A C++ library for parallel graph processing (GRAPE) 🍇
microsoft/ptgnn
A PyTorch Graph Neural Network Library
ML4ITS/mtad-gat-pytorch
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).
kyzhouhzau/NLPGNN
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers
Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.
gasteigerjo/ppnp
PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
jw9730/tokengt
[NeurIPS'22] Tokenized Graph Transformer (TokenGT), in PyTorch
andresprados/SPIGA
SPIGA: Shape Preserving Facial Landmarks with Graph Attention Networks.
Lin-Yijie/Graph-Matching-Networks
PyTorch implementation of Graph Matching Networks, e.g., Graph Matching with Bi-level Noisy Correspondence (COMMON, ICCV 2023), Graph Matching Networks for Learning the Similarity of Graph Structured Objects (GMN, ICML 2019).
TorchSpatiotemporal/tsl
tsl: a PyTorch library for processing spatiotemporal data.
SciML/FluxNeuralOperators.jl
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
NYUMedML/GNN_for_EHR
Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"
vijaydwivedi75/gnn-lspe
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
Liang-ZX/VectorNet
Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”
BUAA-CI-LAB/Literatures-on-GNN-Acceleration
A reading list for deep graph learning acceleration.
basiralab/GNNs-in-Network-Neuroscience
A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.
awslabs/realtime-fraud-detection-with-gnn-on-dgl
An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.
BaeSeulki/NL2LF
The Resources for "Natural Language to Logical Form" ; "自然语言转逻辑形式"研究资料收集。
benedekrozemberczki/SEAL-CI
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
THUDM/GRAND
Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"
datawhalechina/grape-book
图深度学习（葡萄书），在线阅读地址： https://datawhalechina.github.io/grape-book
xmindflow/Awesome_Mamba
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis