Displaying 1 to 10 from 10 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.

SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

  •    Python

SuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of SuperPoint keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair. Full paper PDF: SuperGlue: Learning Feature Matching with Graph Neural Networks.

graph-learn - An Industrial Graph Neural Network Framework

  •    C++

Graph-Learn (formerly AliGraph) is a distributed framework designed for the development and application of large-scale graph neural networks. It abstracts a set of programming paradigms suitable for common graph neural network models from the practical problems of large-scale graph training, and has been successfully applied to many scenarios such as search recommendation, network security, knowledge graph, etc. within Alibaba. Graph-Learn provides both Python and C++ interfaces for graph sampling operations, and provides a gremlin-like GSL (Graph Sampling Language) interface. For upper layer graph learning models, Graph-Learn provides a set of paradigms and processes for model development. It is compatible with TensorFlow and PyTorch, and provides data layer, model layer interfaces and rich model examples.

GraphScope - GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba

  •    Rust

GraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology: including GRAPE, MaxGraph, and Graph-Learn (GL) for analytics, interactive, and graph neural networks (GNN) computation, respectively, and the vineyard store that offers efficient in-memory data transfers. Visit our website at graphscope.io to learn more.

dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.

  •    Python

DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks.

jraph - A Graph Neural Network Library in Jax

  •    Python

Jraph (pronounced "giraffe") is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilities for working with graphs, and a 'zoo' of forkable graph neural network models. Jraph is designed to provide utilities for working with graphs in jax, but doesn't prescribe a way to write or develop graph neural networks.

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.

phc-gnn - Implementation of the Paper: "Parameterized Hypercomplex Graph Neural Networks for Graph Classification" by Tuan Le, Marco Bertolini, Frank Noé and Djork-Arné Clevert

  •    Python

Here we provide the implementation of Parameterized Hypercomplex Graph Neural Networks (PHC-GNNs) in PyTorch Geometric, along with 6 minimal execution examples in the benchmarks/ directory. Generally speaking, the phc/hypercomplex/ subdirectory also includes the quaternion-valued GNN, with the modification to only work on torch.Tensor objects. The phc/quaternion/ subdirectory was first implemented with the fixed rules of the quaternion-algebra, such as how to perform addition, and multiplication which can be summarized in the quaternion-valued affine transformation. The phc/hypercomplex/ directory generalizes such operations to work directly on torch.Tensor objects, making it applicable to many already existing projects. For completeness and to share our initial motivation of this project, we also provide the implementations from the phc/quaternion/ subdirectory.

GNNLens2 - Visualization tool for Graph Neural Networks

  •    TypeScript

GNNLens2 is an interactive visualization tool for graph neural networks (GNN). It allows seamless integration with deep graph library (DGL) and can meet your various visualization requirements for presentation, analysis and model explanation. It is an open source version of GNNLens with simplification and extension. A video demo is available here. Switch the video quality for the best viewing experience.

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