# GNNS

**Repository Path**: gnn4-cd/GNNS

## Basic Information

- **Project Name**: GNNS
- **Description**: No description available
- **Primary Language**: Python
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-21
- **Last Updated**: 2025-02-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: unsupervised

## README

# GNNS

This repo is the authors' implementation of the GNN-style algorithm for community detection described in the paper 
"[Graph Neural Network Inspired Algorithm for Unsupervised Network Community Detection](https://arxiv.org/abs/2103.02520)".

This paper proposes a new variant of the recurrent graph neural network algorithm for unsupervised network _community detection through modularity optimization_. The new algorithm's performance is compared against state-of-the-art methods. The approach also serves as a proof-of-concept for the broader application of recurrent graph neural networks to unsupervised network optimization.

[GNNS.ipynb](https://github.com/Alexander-Belyi/GNNS/blob/master/GNNS.ipynb) notebook is ready to be run in Google Colab. Just follow the link:
https://colab.research.google.com/github/Alexander-Belyi/GNNS/blob/master/GNNS.ipynb.
It should reproduce the results presented in the paper (enable the GPU backend to reproduce the running time figures).

If you find this work useful, please, consider citing:
``` 
S. Sobolevsky, A. Belyi, "Graph Neural Network Inspired Algorithm for Unsupervised Network Community Detection" arXiv preprint arXiv:2103.02520
```