Message Passing Neural Networks for Simplicial and Cell Complexes
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Updated
Jun 13, 2023 - Python
Message Passing Neural Networks for Simplicial and Cell Complexes
Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)
Official repository for Self-Attention Message Passing for Contrastive Few-Shot Learning
Lorentz group equivariant autoencoders based on Lorentz Group Network
Understanding and Extending Subgraph GNNs by Rethinking their Symmetries (NeurIPS 2022 Oral)
Message-Passing Neural Network for Chemistry
Measuring generalization properties of graph neural networks
Official repository for On Over-Squashing in Message Passing Neural Networks (ICML 2023)
GGPM - GraphNN Generation of Organic Photovoltaic Molecules
Efficient Subgraph GNNs by Learning Effective Selection Policies (ICLR 2024)
Graph neural network autoencoders for jets in HEP
unknown edge & node prediction , more
GNN trained on the ZINC dataset (graph-level regression)
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
Graph Neural Network creation module, implemented in Tensorflow 2 with examples using the module and the iGNNition library for fast GNN prototyping.
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.
GNN for predicting absorption spectra. Architecture uses Message Passing Framework to understand molecular features.
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