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.

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},
}