Scalable Attributed-Graph Subspace Clustering (SASGC)

This repository provides Python code to reproduce experiments from the AAAI 2023 paper Scalable Attributed-Graph Subspace Clustering.

Run Experiments

Parameter List for run.py

Parameter	Type	Default	Description
dataset	string	acm	Name of the graph dataset (acm, dblp, arxiv, pubmed or wiki).
power	integer	2	First power to test.
runs	integer	5	Number of runs.

Best Propagation Orders

Dataset	Propagation order
acm	2
dblp	2
arxiv	54
computers	67
wiki	4
pubmed	100

Example

To run the model on computers for power p=67 and have the average execution time

python run.py --dataset=computers --power 67

Citation

If you use this code please do cite :

@inproceedings{fettal2023scalable,
  title={Scalable Attributed-Graph Subspace Clustering},
  author={Fettal, Chakib and Labiod, Lazhar and Nadif, Mohamed},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  year={2023}
}