This is the source code for paper: Dual Label-Guided Graph Refinement for Multi-View Graph Clustering, accepted at AAAI 2023.
- 'requirements.txt'
- The experiments are conducted on a Linux machine with a NVIDIA GeForce RTX 3070 GPU and Intel(R) Xeon(R) E5-2678 v3 @ 2.50GHz CPU.
ACM, ACM (HR 0.00) and ACM (HR 0.20) datasets are included in ./data/, Texas, DBLP and Chameleon are publicly available, and other synthetic data will be published in the future.
| Dataset | #Clusters | #Nodes | #Features | Graphs | HR |
|---|---|---|---|---|---|
| ACM | 3 | 3025 | 1830 |
|
0.82 0.64 |
| DBLP | 4 | 4057 | 334 |
|
0.80 0.67 0.32 |
| Texas | 5 | 183 | 1703 | 0.09 | |
| Chameleon | 5 | 22777 | 2325 | 0.23 |
| Dataset | #Clusters | #Nodes | #Features | Graphs | HR |
|---|---|---|---|---|---|
| ACM (HR 0.00) | 3 | 3025 | 1830 |
|
0.00 0.00 |
| ACM (HR 0.10) | 3 | 3025 | 1830 |
|
0.10 0.10 |
| ACM (HR 0.20) | 3 | 3025 | 1830 |
|
0.20 0.20 |
| ACM (HR 0.30) | 3 | 3025 | 1830 |
|
0.30 0.30 |
| ACM (HR 0.40) | 3 | 3025 | 1830 |
|
0.40 0.40 |
| ACM (HR 0.50) | 3 | 3025 | 1830 |
|
0.50 0.50 |
# Test DuaLGR on ACM dataset
python DuaLGR.py --dataset 'acm' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on DBLP dataset
python DuaLGR.py --dataset 'dblp' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on Texas dataset
python DuaLGR.py --dataset 'texas' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on Chameleon dataset
python DuaLGR.py --dataset 'chameleon' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on synthetic ACM dataset with HR 0.00
python DuaLGR.py --dataset 'acm00' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on synthetic ACM dataset with HR 0.10
python DuaLGR.py --dataset 'acm01' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on synthetic ACM dataset with HR 0.20
python DuaLGR.py --dataset 'acm02' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on synthetic ACM dataset with HR 0.30
python DuaLGR.py --dataset 'acm03' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on synthetic ACM dataset with HR 0.40
python DuaLGR.py --dataset 'acm04' --train False --use_cuda True --cuda_device 0
# Test DuaLGR on synthetic ACM dataset with HR 0.50
python DuaLGR.py --dataset 'acm05' --train False --use_cuda True --cuda_device 0# Train DuaLGR on ACM dataset
python DuaLGR.py --dataset 'acm' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on DBLP dataset
python DuaLGR.py --dataset 'dblp' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on Texas dataset
python DuaLGR.py --dataset 'texas' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on Chameleon dataset
python DuaLGR.py --dataset 'chameleon' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on synthetic ACM dataset with HR 0.00
python DuaLGR.py --dataset 'acm00' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on synthetic ACM dataset with HR 0.10
python DuaLGR.py --dataset 'acm01' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on synthetic ACM dataset with HR 0.20
python DuaLGR.py --dataset 'acm02' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on synthetic ACM dataset with HR 0.30
python DuaLGR.py --dataset 'acm03' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on synthetic ACM dataset with HR 0.40
python DuaLGR.py --dataset 'acm04' --train True --use_cuda True --cuda_device 0
# Train DuaLGR on synthetic ACM dataset with HR 0.50
python DuaLGR.py --dataset 'acm05' --train True --use_cuda True --cuda_device 0Parameters: More parameters and descriptions can be found in the script and paper.
| NMI% | ARI% | ACC% | F1% | |
|---|---|---|---|---|
| ACM | 73.2 | 79.4 | 92.7 | 92.7 |
| DBLP | 75.5 | 81.7 | 92.4 | 91.8 |
| Texas | 36.6 | 27.8 | 57.4 | 43.3 |
| Chameleon | 18.6 | 13.5 | 42.1 | 41.1 |
| ACM (HR 0.00) | 61.6 | 68.5 | 88.3 | 88.3 |
| ACM (HR 0.10) | 62.0 | 69.1 | 88.6 | 88.5 |
| ACM (HR 0.20) | 62.9 | 70.0 | 89.0 | 89.0 |
| ACM (HR 0.30) | 63.7 | 71.0 | 89.4 | 89.4 |
| ACM (HR 0.40) | 93.7 | 96.5 | 98.8 | 98.8 |
| ACM (HR 0.50) | 99.5 | 99.8 | 99.9 | 99.9 |
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