| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| utils.py | 2025-06-04 | 4.4 kB | |
| sim_loss.py | 2025-06-04 | 1.8 kB | |
| sim_model.py | 2025-06-04 | 3.0 kB | |
| batch_sampler.py | 2025-06-04 | 6.9 kB | |
| clustering_metrics.py | 2025-06-04 | 3.5 kB | |
| clustering_module.py | 2025-06-04 | 2.9 kB | |
| plot_clusters.py | 2025-06-04 | 967 Bytes | |
| Totals: 7 Items | 23.4 kB | 0 | |
Graph Clustering via Similarity-Aware Embeddings (GCSAE)
Deep graph clustering via RBF-Optimized embeddings with self-supervised learning
Requirements
[!NOTE] Higher versions should be also compatible.
- torch
- torchvision
- torchaudio
- torch-scatter
- torch-sparse
- torch-cluster
- munkres
- kmeans-pytorch
- Scipy
- Scikit-learn
pip install -r requirements.txt
Model
Reproduction
The same code can be used for Citeseer, Amazon-Photo and Amazon-Computers by changing the dataset name.
- Cora
!python train.py --runs 1 --dataset 'Computers' --hidden '512' --1_1 100 --l_2 --tau 0.5 --ns 0.5 --lr 0.0005 --epochs_sim 150 --epochs_cluster 150 --wd 1e-3 --alpha 0.9