Build Graph Nets in Tensorflow

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

TypeDB-ML is the Machine Learning integrations library for TypeDB

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

A list of interesting graph neural networks (GNN) links with a primary interest in recommendations and tensorflow that is continually updated and refined

A toolkit for mapping networks of political and economic influence through diverse types of entities and their relations. Accessible at http://granoproject.org

[CVPR2019]Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

[NeurIPS 2023] Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs

Graph Network for protein-protein interface

Graph convolutions in Keras with TensorFlow, PyTorch or Jax.

Reimplementation of Learning Mesh-based Simulation With Graph Networks

Implements a disparity filter in Python, based on graphs in NetworkX, to extract the multiscale backbone of a complex weighted network (Serrano, et al., 2009)

Graph Neural networks for NLP

Graph Network for protein-protein interface including language model features

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Graph Nets (GN) implement by pytorch

This project involved the analysis of the ArXiv citation network.

Analysis of the Symptoms-Disease Network database using communities.

Explainability of Deep RL algorithms using graph networks and layer-wise relevance propagation.