https://github.com/snap-research/cabam-graph-generation
[KDD MLG'20] Class-Assortative Barabasi Albert Model for Graph Generation
https://github.com/snap-research/cabam-graph-generation
graph-generation graph-generator graph-neural-networks
Last synced: 3 months ago
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[KDD MLG'20] Class-Assortative Barabasi Albert Model for Graph Generation
- Host: GitHub
- URL: https://github.com/snap-research/cabam-graph-generation
- Owner: snap-research
- License: mit
- Created: 2023-01-13T17:15:35.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-01-20T03:41:08.000Z (over 2 years ago)
- Last Synced: 2025-01-16T09:55:39.248Z (4 months ago)
- Topics: graph-generation, graph-generator, graph-neural-networks
- Language: Jupyter Notebook
- Homepage:
- Size: 23.8 MB
- Stars: 1
- Watchers: 5
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Scale-Free, Attributed and Class-Assortative Graph Generation
This repository contains code for the paper ["Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks,"](https://nshah.net/publications/CABAM.MLG.2020.pdf) published at KDD MLG 2020. In particular, it enables use of the CABAM (Class Assortative Barabasi Albert Model) for graph generation, which enhances the traditional Barabasi-Albert preferential attachment model with flexible class labels, feature distributions and assortativity (homophily) parameters.
## Installation
You can install a `cabam` package with
```
pip install .
```
## Downloading Data
You can see `./Dataset Summaries.ipynb` for reproducing dataset statistics mentioned in the paper.
Data referenced in this code is sourced from versions available at
[graph2gauss](https://github.com/abojchevski/graph2gauss) and
[GAug](https://github.com/zhao-tong/GAug) repositories.
Please run:
```
bash download_data.sh
```
in order to download all relevant data files referenced in scripts here (all files will be placed under `./data`), before trying to run notebook commands yourself.
## Generating graphs with CABAM
The below is a basic example, to use CABAM to generate a graph with with 1000 nodes and minimum degree of 3,
with two balanced classes (default) and roughly equal numbers of homophilous and heterophilous edges (default).
```
from cabam import CABAM
model = CABAM()
G, degrees, labels, num_intra_edges, num_inter_edges = model.generate_graph(n=1000, m=3)
```
See `generate_graph(...)` in `./cabam/cabam.py` for customizing use.
## Reference
If you use the model, or graphs generated with the model for evaluation in your own work, please cite
```BibTeX
@inproceedings{cabam2020shah,
author = {Shah, Neil},
title = {Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks},
booktitle = {KDD Mining and Learning with Graphs},
year = {2020}
}
```