https://github.com/ermongroup/graphscorematching
Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling
https://github.com/ermongroup/graphscorematching
Last synced: 5 months ago
JSON representation
Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling
- Host: GitHub
- URL: https://github.com/ermongroup/graphscorematching
- Owner: ermongroup
- License: gpl-3.0
- Created: 2020-02-27T17:22:48.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T21:42:56.000Z (over 2 years ago)
- Last Synced: 2025-03-31T16:13:18.753Z (6 months ago)
- Language: Python
- Size: 6.45 MB
- Stars: 110
- Watchers: 11
- Forks: 20
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Permutation Invariant Graph Generation via Score-Based Generative Modeling
This repo contains the official implementation for the paper
[Permutation Invariant Graph Generation via Score-Based Generative Modeling](http://proceedings.mlr.press/v108/niu20a) (AISTATS 2020),
Authors: Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
-------------------------------------------------------------------------------------
We propose a permutation invariant approach to modeling graphs, using the framework of score-based generative modeling. In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (_a.k.a_, the score function). This permutation equivariant model of gradients implicitly defines a permutation invariant distribution for graphs. We can train this graph neural network with score matching and sample from it with annealed Langevin dynamics.
## Dependencies
First, install PyTorch following the steps on its [official website](https://pytorch.org/). The code has been tested over PyTorch 1.3.1 and 1.8.1.
Then run the following command to install the other dependencies.
```shell
pip install -r requirements.txt
```
To compile the ORCA program (see http://www.biolab.si/supp/orca/orca.html) for the evaluation step, run
```shell
cd evaluation/orca && g++ -O2 -std=c++11 -o orca orca.cpp
```
## Running Experiments
### Preparing Datasets
To generate the datasets, run
```shell
mkdir data
python gen_data.py # to generate the community-small dataset
python process_dataset.py # to generate the ego-small dataset
```
### Configuring
The configurations are in the `config/` directory, written in the `YAML` format. Refer to the comments in the given files for details.
The output files under the directory / (set in the `YAML` configuration file) are
```shell
.
├── config.yaml # a copy of the configuration
├── fig # reconstruction of the perturbed graphs
│ └── xxx.pdf
├── info.log # logs (if running train.py)
├── models
│ └── xxx.pth # the saved PyTorch checkpoint
└── sample
├── fig
│ └── xxx.pdf # images of the generated graphs
├── info.log # logs (if running sampling.py)
└── sample_data
└── xxx.pkl # saved python list object of the generated graphs (networkx.Graph)
```
### Training
`train.py` is the main executable file to run the whole pipeline (training, sampling, evaluation). Run `python train.py -h` to show its usage:
``` text
usage: train.py [-h] -c CONFIG_FILE [-l LOG_LEVEL] [-m COMMENT]
Running Experiments
optional arguments:
-h, --help show this help message and exit
-c CONFIG_FILE, --config_file CONFIG_FILE
Path of config file
-l LOG_LEVEL, --log_level LOG_LEVEL
Logging Level, one of: DEBUG, INFO, WARNING, ERROR, CRITICAL
-m COMMENT, --comment COMMENT
A single line comment for the experiment
```
Examples:
```shell
python train.py -c config/train_ego_small.yaml # to run on Ego-small dataset
python train.py -c config/train_com_small.yaml # to run on Community-small dataset
```
### Sampling
`sample.py` is for evaluating a saved model. The usage is the same as `train.py`. To set the location of the saved model, change `model_save_dir` in the `YAML` file, e.g. `model_save_dir: 'exp/ego_small/edp-gnn_ego_small_xxx/models'`.
Examples:
```shell
python sample.py -c config/sample_ego_small.yaml # to run on Ego-small dataset
python sample.py -c config/sample_com_small.yaml # to run on Community-small dataset
```