https://github.com/graph-com/graphmaker
[TMLR] GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
https://github.com/graph-com/graphmaker
ddpm generative-model graph-generation graph-neural-networks network-science node-classification pytorch social-network synthetic-data-generation
Last synced: 9 months ago
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[TMLR] GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
- Host: GitHub
- URL: https://github.com/graph-com/graphmaker
- Owner: Graph-COM
- License: mit
- Created: 2023-10-04T17:56:18.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-25T04:36:23.000Z (9 months ago)
- Last Synced: 2025-04-25T05:31:43.200Z (9 months ago)
- Topics: ddpm, generative-model, graph-generation, graph-neural-networks, network-science, node-classification, pytorch, social-network, synthetic-data-generation
- Language: Python
- Homepage:
- Size: 1.86 MB
- Stars: 56
- Watchers: 2
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GraphMaker
[[Paper]](https://arxiv.org/abs/2310.13833)
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
* [Train](#train)
* [Sample](#sample)
* [Sample with Pre-Trained Models](#sample-with-pre-trained-models)
- [Frequently Asked Questions](#frequently-asked-questions)
* [Q1: libcusparse.so](#q1-libcusparseso)
* [Q2: Wandb](#q2-wandb)
* [Q3: Other Requests](#q3-other-requests)
- [Citation](#citation)
## Installation
```bash
conda create -n GraphMaker python=3.8 -y
conda activate GraphMaker
pip install torch==1.12.0+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
conda install -c conda-forge cudatoolkit=11.6
pip install dgl==1.1.0+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html
pip install pandas scikit-learn pydantic wandb huggingface_hub==0.30.2
```
You also need to compile `orca.cpp` (https://file.biolab.si/biolab/supp/orca/orca.html).
```bash
cd orca
g++ -O2 -std=c++11 -o orca orca.cpp
```
## Usage
### Train
```bash
# The GraphMaker-Sync variant simultaneously generates node attributes and graph structure.
python train_sync.py -d D
# The trained model checkpoint will be saved to {D}_cpts/Sync_XXX.pth
# The GraphMaker-Async variant first generates node attributes, and then generates graph structure.
python train_async.py -d D
# The trained model checkpoint will be saved to {D}_cpts/Async_XXX.pth
```
`D` can be one of the three built-in datasets, including `cora`, `amazon_photo`, `amazon_computer`.
### Sample
```bash
python sample.py --model_path P
```
`P` is the path to a model checkpoint saved in the training stage.
### Sample with Pre-Trained Models
Alternatively, you can also use our pre-trained model checkpoints for sampling.
```bash
python sample.py --dataset D --type T
```
- `D` can be one of the three built-in datasets, including `cora`, `amazon_photo`, `amazon_computer`.
- `T` can be `sync` or `async`.
## Frequently Asked Questions
### Q1: libcusparse.so
**An error occurs that the program cannot find `libcusparse.so`.**
To search for the location of it on linux,
```bash
find /path/to/directory -name libcusparse.so.11 -exec realpath {} \;
```
where `/path/to/directory` is the directory you want to search. Assume that the search returns `home/miniconda3/envs/GraphMaker/lib/libcusparse.so.11`. Then you need to manually specify the environment variable as follows.
```bash
export LD_LIBRARY_PATH=home/miniconda3/envs/GraphMaker/lib:$LD_LIBRARY_PATH
```
### Q2: Wandb
**What is WandB?**
[WandB](https://wandb.ai/site) is a tool for visualizing and tracking your machine learning experiments. It's free to use for open source projects. You may also use our code without it.
### Q3: Other Requests
**I have a question or request not listed here.**
- It's generally recommended to open a GitHub issue. This allows us to track the progress, and the discussion might help others who have the same question.
- Otherwise, you can also send an email to `mufeili1996@gmail.com`.
## Citation
```tex
@article{li2024graphmaker,
title={GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?},
author={Mufei Li and Eleonora Kreačić and Vamsi K. Potluru and Pan Li},
journal={Transactions on Machine Learning Research},
year={2024}
}
```