Graph Learning Python Packages

thegolem

Graph Optimiser for Learning and Evolution of Models

pgl

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

neural-structured-learning

Training neural models with structured signals.

snore-embedding

SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

mantra-dataset

MANTRA: The Manifold Triangulations Assemblage (A dataset of manifold triangulations)

deltaconv

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

dglke

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

graphnetz

A benchmark whose default output is a statistical report, not a leaderboard.

geome

Geometric Learning for Genome Data

torch-quiver

PyTorch Library for Low-Latency, High-Throughput Graph Learning on GPUs.

graph-structure-learning

Extracting graphs from signals on nodes

rings-evaluation

RINGS: Relevant Information in Node Features and Graph Structure. An evaluation framework for graph-learning based on first principles.

coperception

A library for collaborative perception.

euler-gl

A toolset for network representation learning.

euler2-gl

A distributed graph deep learning framework.