Graph Neural Networks
Showing 16 of 16 projects
A PyTorch library for building and training Graph Neural Networks (GNNs) on structured and irregular data.
A comprehensive library for building and training Graph Neural Networks (GNNs) with PyTorch.
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.
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