Awesome
Graph Convolutional Clustering
This repository provides Python (Tensorflow) code to reproduce experiments from the WSDM '22 paper Efficient Graph Convolution for Joint Node Representation Learning and Clustering.
<img src="./schematic.png">Installation
python setup.py install
Run Experiments
Parameter list
For run.py
| Parameter | Type | Default | Description |
|---|---|---|---|
dataset | string | cora | Name of the graph dataset (cora, citeseer, pubmed or wiki). |
power | integer | 5 | First power to test. |
runs | integer | 20 | Number of runs per power. |
n_clusters | integer | 0 | Number of clusters (0 for ground truth). |
max_iter | integer | 30 | Number of iterations of the algorithm. |
tol | float | 10e-7 | Tolerance threshold of convergence. |
For tune_power.py parameters are the same except for power which is replaced by
| Parameter | Type | Default | Description |
|---|---|---|---|
min_power | integer | 1 | Smallest propagation order to test. |
max_power | integer | 150 | Largest propagation order to test. |
Best Propagation Orders
| Dataset | Propagation order |
|---|---|
citeseer | 5 |
cora | 12 |
pubmed | 150 |
wiki | 4 |
Example
To adaptively tune the power on Cora use
python gcc/tune_power.py --dataset=cora
To run the model on Cora for power p=12 and have the average execution time
python gcc/run.py --dataset=cora --power 12
Citation
Please cite the following paper if you used GCC in your research
@inproceedings{fettal2022efficient,
author = {Fettal, Chakib and Labiod, Lazhar and Nadif, Mohamed},
title = {Efficient Graph Convolution for Joint Node Representation Learning and Clustering},
year = {2022},
publisher = {Association for Computing Machinery},
doi = {10.1145/3488560.3498533},
booktitle = {Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
pages = {289–297},
}