Graph Learning Python Packages

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.

thegolem

Graph Optimiser for Learning and Evolution of Models

deltaconv

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

mdl-fca

Learning compact concept DAGs from binary data by minimum description length — a probabilistic take on Formal Concept Analysis

mantra-dataset

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

dglke

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

geome

Geometric Learning for Genome Data

snore-embedding

SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

graphnetz

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

graph-dependence

Reference implementation for separating graph dependence from graph advantage in graph-ablation studies.

graph-structure-learning

Collection of models for learning networks from signals.

rings-evaluation

Relevant Information in Node features and Graph Structure (ICML 2025)

coperception

An SDK for multi-agent collaborative perception.

torch-quiver

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

euler-gl

A distributed graph deep learning framework.

euler2-gl

A distributed graph deep learning framework.