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This repository provides Python (Tensorflow) code to reproduce experiments from the WSDM '22 paper "Efficient Graph Convolutional Representation Learning for Graph Clustering".

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graph_convolutional_clustering's Introduction

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},
}

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