pytorch-geometric

Transform geospatial relations into graphs for Graph Neural 网络s and spatial network analysis

Protein Graph Library

An autoML framework & toolkit for machine learning on graphs.

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural 网络s (ICML 2023)

Free hands-on course about Graph Neural 网络s using PyTorch Geometric.

Implementation of Principal Neighbourhood Aggregation for Graph Neural 网络s in PyTorch, DGL and PyTorch Geometric

Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural 网络s" in PyG.

gRNAde is a Generative AI framework for inverse design of 3D RNA structure and function

The official implementation for ICLR23 spotlight paper "DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion"

The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"

Attention over nodes in Graph Neural 网络s using PyTorch [NeurIPS 2019]

A PyTorch implementation of "Signed Graph Convolutional 网络" (ICDM 2018).

[CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"

GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]

B站GNN教程资料

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural 网络s via Historical Embeddings" in PyTorch

PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.

Topological Graph Neural 网络s (ICLR 2022)

Graph Neural 网络 application in predicting AC Power Flow calculation. Developed with Pytorch Geometric framework. My Master Thesis at Eindhoven University of Technology