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[WSDM 2022] Efficient Graph Convolution for Joint Node Representation Learning and Clustering

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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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[WSDM 2022] Efficient Graph Convolution for Joint Node Representation Learning and Clustering

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