KDD2022CLARE

This is our implementation for the paper (arXiv):

CLARE: A Semi-supervised Community Detection Algorithm

What are in this Repository

This repository contains the following contents:

.
├── Locator                       --> (The folder containing Community Locator source code)
├── Rewriter                      --> (The folder containing Community Rewriter source code)
├── ckpts                         --> (The folder saving checkpoint files)
├── dataset                       --> (The folder containing 7 used datasets)
├── run.py                        --> (The main code file. The code is run through this file)
└── utils                         --> (The folder containing utils functions)

Note that you have to create a ckpts folder to save contents.

Datasets

Raw datasets are available at SNAP(http://snap.stanford.edu/data/index.html) and pre-processing details are explained in our paper.

We select LiveJournal, DBLP and Amazon, in the Networks with ground-truth communities part.

We provide 7 datasets as below. Each of them contains a community file {name}-1.90.cmty.txt and an edge file {name}-1.90.ungraph.txt.

├── dataset
│   ├── amazon
│   ├── amazon_dblp
│   ├── dblp
│   ├── dblp_amazon
│   ├── dblp_lj
│   ├── lj
│   ├── lj_dblp

Run our code

Environmental Requirement

You need to set up the environment for running the experiments (Python 3.7 or above)
Install Pytorch with version 1.8.0 or later
Install torch-geometric package with version 2.0.1

Note that it may need to appropriately install the package torch-geometric based on the CUDA version (or CPU version if GPU is not available). Please refer to the official website https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html for more information of installing prerequisites.

For example (Mac / CPU)
```
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.9.0+cpu.html
```
Install Deepsnap with version 0.2.1
```
pip install deepsnap
```

Run the code

Execute the run.py file

python run.py --dataset=amazon --conv_type=GCN

Main arguments:

--dataset [amazon, dblp, lj, amazon_dblp, dblp_amazon, dblp_lj, lj_dblp]: the dataset to run
--conv_type [GCN, GIN, SAGE]: GNN type in Community Locator
--n_layers: ego-net dimensions & number of GNN layers
--pred_size: total number of predicted communities
--agent_lr: the learning rate of Community Rewriter

For more argument options, please refer to run.py

Cite

Please cite our paper if you make advantage of the CLARE in your own work:

@inproceedings{wu2022clare,
  title={CLARE: A Semi-supervised Community Detection Algorithm},
  author={Wu, Xixi and Xiong, Yun and Zhang, Yao and Jiao, Yizhu and Shan, Caihua and Sun, Yiheng and Zhu, Yangyong and Philip S. Yu},
  booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2022},
  organization={ACM}
}