There are 26 repositories under gnn topic.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
An Industrial Graph Neural Network Framework
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
A repository of pretty cool datasets that I collected for network science and machine learning research.
Code and resources on scalable and efficient Graph Neural Networks
🍇 A C++ library for parallel graph processing (GRAPE) 🍇
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
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).
Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.
SPIGA: Shape Preserving Facial Landmarks with Graph Attention 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).
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
tsl: a PyTorch library for processing spatiotemporal data.
Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
A reading list for deep graph learning acceleration.
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
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.
A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.