Graph Neural Network creation module, implemented in Tensorflow 2 with examples using the module and the iGNNition library for fast GNN prototyping.
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Updated
Jun 17, 2021 - Python
Graph Neural Network creation module, implemented in Tensorflow 2 with examples using the module and the iGNNition library for fast GNN prototyping.
unknown edge & node prediction , more
Message-Passing Neural Network for Chemistry
GGPM - GraphNN Generation of Organic Photovoltaic Molecules
Master thesis: JAT (Jraph Attention Networks), a deep learning architecture to predict the potential energy and forces of molecules. Adapts Graph Attention Networks (GATv2) within the Message Passing Neural Networks framework to computational chemistry in JAX
Understanding and Extending Subgraph GNNs by Rethinking their Symmetries (NeurIPS 2022 Oral)
Graph neural network autoencoders for jets in HEP
Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)
Message Passing Neural Networks for Simplicial and Cell Complexes
Measuring generalization properties of graph neural networks
Lorentz group equivariant autoencoders based on Lorentz Group Network
Official repository for On Over-Squashing in Message Passing Neural Networks (ICML 2023)
A collection of projects using graph neural networks implemented from first principles, and using the PyTorch Geometric library
A project utilizing graph neural network to predict BBBP, featuring a modular model architecture.
Official repository for Self-Attention Message Passing for Contrastive Few-Shot Learning
GNN for predicting absorption spectra. Architecture uses Message Passing Framework to understand molecular features.
Efficient Subgraph GNNs by Learning Effective Selection Policies (ICLR 2024)
GNN trained on the ZINC dataset (graph-level regression)
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