GraphEm Rapids Documentation
High-performance graph embedding using PyTorch and RAPIDS cuVS. Force-directed layout with geometric intersection detection.
Features
Unified API: Scipy sparse adjacency matrices, sklearn-style parameters
Multiple Backends: PyTorch (1K-100K vertices), RAPIDS cuVS (100K+ vertices)
GPU Acceleration: CUDA support, memory-efficient chunking, automatic CPU fallback
Graph Generators: Erdős-Rényi, scale-free, SBM, bipartite, Delaunay, and more
Influence Maximization: Fast embedding-based seed selection
Quick Start
Installation:
pip install graphem-rapids # PyTorch backend
pip install graphem-rapids[cuda] # + CUDA
pip install graphem-rapids[rapids] # + RAPIDS cuVS
pip install graphem-rapids[all] # Everything
Basic Usage:
import graphem_rapids as gr
# Generate graph
adjacency = gr.generate_er(n=1000, p=0.01)
# Create embedder (automatic backend selection)
embedder = gr.create_graphem(adjacency, n_components=3)
# Run layout
embedder.run_layout(num_iterations=50)
# Get positions and visualize
positions = embedder.get_positions()
embedder.display_layout()
Contents
Citation
@misc{kolpakov-rivin-2025fast,
title={Fast Geometric Embedding for Node Influence Maximization},
author={Kolpakov, Alexander and Rivin, Igor},
year={2025},
eprint={2506.07435},
archivePrefix={arXiv},
primaryClass={cs.SI},
url={https://arxiv.org/abs/2506.07435}
}