Graph Neural Networks
Showing 32 of 32 projects
A comprehensive library for building and training Graph Neural Networks (GNNs) with PyTorch.
A PyTorch library for building and training Graph Neural Networks (GNNs) on structured and irregular data.
A Python package for deep learning on graphs, framework-agnostic and optimized for performance and scalability.
DeepMind's library for building graph networks in TensorFlow and Sonnet, enabling graph-structured data processing with neural networks.
A curated collection of links to conference publications, surveys, and software in graph-based deep learning.
A curated collection of graph classification papers with implementations covering embeddings, deep learning, kernels, and factorization.
A curated collection of graph classification papers with reference implementations covering embedding, deep learning, kernels, and factorization.
A unified, comprehensive, and efficient Python/PyTorch library for reproducing and developing recommendation algorithms.
A Python library for machine learning on graphs and networks, offering state-of-the-art algorithms for tasks like node classification and link prediction.
A PyTorch library for spatiotemporal signal processing with dynamic and temporal graph neural networks.
A curated list of network embedding techniques, including papers, implementations, and related resources for graph representation learning.
A Python library for graph deep learning built on Keras and TensorFlow 2, providing flexible tools for graph neural networks.
A machine learning package implementing message passing neural networks for predicting molecular and reaction properties.
A curated collection of research papers and software for explainable graph machine learning and reasoning.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
A TensorFlow library for building Graph Neural Networks with support for heterogeneous graphs and scalable data processing.
A lightweight library for building and training graph neural networks using JAX.
A lightweight library for building and training graph neural networks in JAX, providing graph data structures, utilities, and model implementations.
An autoML framework and toolkit for automating machine learning tasks on graph-structured data.
A PyTorch framework for semantic segmentation of large 3D point clouds using superpoint graphs.
A Python package for applying graph neural networks to molecular graphs and biological networks in life science research.
A PyTorch and TorchDrug based deep learning library for drug pair scoring, predicting interactions, side effects, and synergy.
A deep learning library built on Chainer for molecular property prediction using graph convolutional neural networks.
A junction tree variational autoencoder for generating valid molecular graphs with desired chemical properties.
A machine learning integrations library for TypeDB, enabling graph algorithms and Graph Neural Networks on strongly-typed graph data.
A deep learning framework for integrating single-cell multi-omics data using graph-linked unified embeddings.
A geometric deep learning model that predicts transcriptional outcomes of single and multi-gene perturbations from single-cell RNA-seq data.
GraphDTA predicts drug-target binding affinity using graph neural networks for drug discovery.
A PyTorch implementation combining Graph Convolutional Networks with OpenNMT-py for structured data to text generation.
A PyTorch Geometric extension library for signed and directed graph neural networks, embedding, and clustering methods.
A PyTorch-based toolbox for graph reliability, focusing on adversarial attacks, defenses, and robustness techniques for graph neural networks.
A JAX + Flax implementation of physics-inspired graph neural networks for solving combinatorial optimization problems like Max-Cut and Maximum Independent Set.
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