Displaying 1 to 6 from 6 results

stellargraph - StellarGraph - Machine Learning on Graphs

  •    Python

StellarGraph is a Python library for machine learning on graphs and networks. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn, so it is easy to augment the core graph machine learning algorithms provided by StellarGraph. It is thus also easy to install with pip or Anaconda.

pytorch_geometric - Geometric Deep Learning Extension Library for PyTorch

  •    Python

PyTorch Geometric is a geometric deep learning extension library for PyTorch. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.

kglib - Grakn Knowledge Graph Library (ML R&D)

  •    Python

To respond to these scenarios, KGLIB is the centre of all research projects conducted at Grakn Labs. In particular, its focus is on the integration of machine learning with the Grakn Knowledge Graph. More on this below, in Knowledge Graph Tasks. At present this repo contains one project: Knowledge Graph Convolutional Networks (KGCNs). Go there for more info on getting started with a working example.

dgl-lifesci - Python package for graph neural networks in chemistry and biology

  •    Python

We also have a slack channel for real-time discussion. If you want to join the channel, contact mufeili1996@gmail.com. Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in life science such as molecular graphs and biological networks, making it an import area for applying deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science with graph neural networks.

pytorch_cluster - PyTorch Extension Library of Optimized Graph Cluster Algorithms

  •    Cuda

All included operations work on varying data types and are implemented both for CPU and GPU. If you are running into any installation problems, please create an issue. Be sure to import torch first before using this package to resolve symbols the dynamic linker must see.

Curvature-Learning-Framework - Curvlearn, a Tensorflow based non-Euclidean deep learning framework.

  •    Python

Actually perfect embedding without distortion, appearing naturally in hyperbolic (negative curvature) or spherical (positive curvature) space, is infeasible in Euclidean space [1]. As shown above, due to the high capacity of modeling complex structured data, e.g. scale-free, hierarchical or cyclic, there has been an growing interest in building deep learning models under non-Euclidean geometry, e.g. link prediction [2], recommendation [3].

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