Spectral Subspace Clustering for Attributed Graphs

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📝 Envirorment

tensorflow --2.10.0

numpy --1.24.4

networkx --3.1

scikit-learn --1.3.2

scipy --1.10.1

Parameters

Parameter	Type	Default	Description
dataset	string	acm	Name of the graph dataset (acm, dblp, arxiv, pubmed or wiki).
T	integer	10	Propagation order.
alpha	float	0.9	the weight parameter in PowerIteration.
gamma	float	1.0	weight parameter for the second term in modularity maximization.
tau	integer	7	the itertate times to get convergence results.
runs	integer	5	Number of runs.

🚀 Example

You can get the results in paper by running following instruction.

$bash run.sh 

📚 Datasets

You can download all datasets from HERE.

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😀 Contact

For any questions or feedback, feel free to contact Miss Xiaoyang LIN.

🌟 Citation

If you find S2CAG and M-S2CAG useful in your research or applications, please kindly cite:

@inproceedings{lin2024s2cag,
title={Spectral Subspace Clustering for Attributed Graphs}, 
author={Xiaoyang Lin and Renchi Yang and Haoran Zheng and Xiangyu Ke},
booktitle={Proceedings of the 31th ACM SIGKDD conference on knowledge discovery and data mining},
pages={To Appear},
year={2024}
}

😊 Acknowledgements

You may refer to related work that serves as foundations for our framework and code repository, SAGSC, etc. Thanks for their wonderful works.