torch-graph-components 0.1.1

Fast GPU graph components and partitioning algorithms for PyTorch

torch-graph-components

Official PyTorch implementation for the graph components and graph partitioning algorithms from

EZ-SP: Fast and Lightweight Superpoint-Based 3D Segmentation


If you ❤️ or simply use this project, don't forget to give the repository a ⭐, it means a lot to us !

📌 Description

torch-graph-components holds algorithms for fast graph connected components and graph partitioning, fully implemented in PyTorch with GPU parallelization in mind.

In particular, this project contains two main algorithms:

• wcc_by_max_propagation for searching the Weakly Connected Components of a graph in pure PyTorch. We created this to avoid the reliance on SciPy's CPU-based computation found in many PyTorch projects.
• merge_components_by_contour_prior for partitioning a graph based on an energy minimization problem which we solve approximatively with a greedy heuristic. Basically, this algorithm lets you find clusters in a graph based on two objectives:
  • compatibility between values carried by the nodes of a same component
  • complexity of contour of the components

See our paper for more details.

🏗 Installation

1. Install dependencies

torch-graph-components relies on backend libraries (torch, torch-geometric, torch-scatter). These dependencies are not bundled and must be installed manually by the user because they depend on:

• Python version
• PyTorch version
• CUDA version
• Operating system

# Specify your torch and and CUDA versions
export TORCH_VERSION=2.7.0
export CUDA_VERSION=cu126

# Install torch
# https://pytorch.org
pip install torch==${TORCH_VERSION} --index-url https://download.pytorch.org/whl/${CUDA_VERSION}

# Install PyTorch Geometric and PyTorch Scatter
# https://pytorch-geometric.readthedocs.io
pip install torch-scatter torch-geometric -f https://data.pyg.org/whl/torch-${TORCH_VERSION}+${CUDA_VERSION}.html

2. Install the project

pip install torch-graph-components

🚀 Usage

import torch
from torch_graph_components import wcc_by_max_propagation
from torch_graph_components import merge_components_by_contour_prior

device = 'cuda'  # choose your device
N = 10  # number of nodes
E = torch.tensor([
    [0, 2, 3, 2],
    [1, 3, 4, 4],
], dtype=torch.long, device=device)  # edges

X = torch.rand(N, 3, device=device)  # node features
S = torch.ones(N, device=device)  # node weights
W = torch.rand(E.shape[1], device=device)  # edge weights
reg = 0.05  # regularization term
min_size = 1  # minimum component size

# Compute the weakly connected components. Returns the final component
# assignment as a tensor of node-wise indices. 
# See code documentation for more details
I_wcc, _ = wcc_by_max_propagation(N, E, verbose=True)
print(I_wcc)

# Partition the graph. Returns the final component assignment as a 
# tensor of node-wise indices, along with other merged attributes. 
# See code documentation for more details
I_merged, _, _ = merge_components_by_contour_prior(
    X,
    S,
    E,
    W,
    reg,
    min_size,
    verbose=True)
print(I_merged)

👩‍🔧 Troubleshooting

Here are some common issues and tips for tackling them.

⚠️ If you experience:

• segmentation faults
• crashes during import
• low-level runtime errors

These issues are almost always caused by incompatible backend dependencies installations. Please ensure that the dependencies were properly installed by running, in python:

import torch
import torch_geometric
import torch_scatter

💬 Citing our work

If your work uses all or part of the present code, please include the following a citation:

@article{geist2025ezsp,
  title={EZ-SP: Fast and Lightweight Superpoint-Based 3D Segmentation},
  author={Geist, Louis and Landrieu, Loic and Robert, Damien},
  journal={arXiv},
  year={2025},
}

Also, if you ❤️ or simply use this project, don't forget to give the repository a ⭐, it means a lot to us !