Displaying 1 to 8 from 8 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.

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.

gossipnet - Non-maximum suppression for object detection in a neural network

  •    Python

This is the code for the paper Learning non-maximum suppression. Jan Hosang, Rodrigo Benenson, Bernt Schiele. CVPR 2017. Run make to compile C++ code and protobufs.




bayesgrad - BayesGrad: Explaining Predictions of Graph Convolutional Networks

  •    Jupyter

The paper is available on arXiv, https://arxiv.org/abs/1807.01985. From left: tox21 pyridine (C5H5N), tox21 SR-MMP, delaney solubility visualization.

chainer-chemistry - Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry

  •    Python

Chainer Chemistry is a collection of tools to train and run neural networks for tasks in biology and chemistry using Chainer[1]. It supports various state-of-the-art deep learning neural network models (especially Graph Convolution Neural Network) for chemical molecule property prediction.

chainer-graph-cnn - Chainer implementation of 'Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering' (https://arxiv

  •    Python

Disclaimer: PFN provides no warranty or support for this implementation. Use it at your own risk. See license for details. This is not the original author's implementation. This implementation was based on https://github.com/mdeff/cnn_graph.

decagon - Graph convolutional neural network for multirelational link prediction

  •    Jupyter

This repository contains code necessary to run the Decagon algorithm. Decagon is a method for learning node embeddings in multimodal graphs, and is especially useful for link prediction in highly multi-relational settings. See our paper for details on the algorithm. Decagon is used to address a burning question in pharmacology, which is that of predicting safety of drug combinations.