graph-learning

A distributed graph deep learning framework.

Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.

Training neural models with structured signals.

Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)

PyTorch Library for Fast and Easy Distributed Graph Learning

Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm 🍹https://arxiv.org/abs/2010.01196

Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."

[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception

GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding

Neuro-symbolic interpretation learning (mostly just language-learning, for now)

[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation

Topological Graph Neural Networks (ICLR 2022)

SDK for multi-agent collaborative perception.

Paper Lists for Fair Graph Learning (FairGL).

Lifelong Graph Learning (CVPR 2022 Oral)

Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection (ECCV 2022)

Graph Optimiser for Learning and Evolution of Models