Open-source code for "RELIANT: Fair Knowledge Distillation for Graph Neural Networks".
If you find our code and the new datasets (folders named small and medium) for fairness research on graphs useful, please cite our paper. Thank you!
@inproceedings{dong2023reliant,
title={RELIANT: Fair Knowledge Distillation for Graph Neural Networks},
author={Dong, Yushun and Zhang, Binchi and Yuan, Yiling and Zou, Na and Wang, Qi and Li, Jundong},
booktitle={Proceedings of the 2023 SIAM International Conference on Data Mining},
year={2023}
}
Experiments are carried out on an Nvidia RTX A6000 with Cuda 11.1.
Library details can be found in requirements.txt.
Notice: Cuda is enabled for default settings.
We have three datasets for experiments, namely Recidivism, Credit, DBLP, and DBLP-L.
First enter the directory
cd CPF-master
Run
python train_dgl.py --dataset small --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:23<00:00, 43.45it/s]
Optimization Finished!
Total time elapsed: 13.8847s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'GCN', 42) | 0.2497±0.0000 | 0.9222±0.0000 | 0.0676±0.0000 | 0.0182±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset small --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
Loading cascades...
62%|███████████████████████████████████████████████████████████████████████████████████████████████▍ | 616/1000 [12:30<07:49, 1.22s/it]Stop!!!
62%|███████████████████████████████████████████████████████████████████████████████████████████████▍ | 616/1000 [12:31<07:48, 1.22s/it]
Optimization Finished!
Total time elapsed: 643.3283s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'PLP', 42) | 0.9303±0.0000 | 0.9292±0.0000 | 0.0217±0.0000 | 0.1029±0.0000 |
Run
python train_dgl.py --dataset small --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
72%|████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 723/1000 [00:17<00:06, 41.83it/s]Saving cascade info...
72%|████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 723/1000 [00:27<00:10, 26.40it/s]
Optimization Finished!
Total time elapsed: 12.5953s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('small', 'GraphSAGE', 42) | 0.2195±0.0000 | 0.9235±0.0000 | 0.0812±0.0000 | 0.0612±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset small --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
Loading cascades...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [21:40<00:01, 1.25s/it]Stop!!!
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [21:41<00:01, 1.30s/it]
Optimization Finished!
Total time elapsed: 1109.8062s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'PLP', 42) | 0.9287±0.0000 | 0.9318±0.0000 | 0.0315±0.0000 | 0.0711±0.0000 |
Run
python train_dgl.py --dataset small --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:24<00:00, 41.41it/s]
Optimization Finished!
Total time elapsed: 14.2494s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'GCN', 42) | 0.2350±0.0000 | 0.9204±0.0000 | 0.0733±0.0000 | 0.0370±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset small --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 92.036%
Teacher Parity SCORE: 0.07330734697729868
Teacher Equality SCORE: 0.03696411929170551
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [01:21<00:00, 7.38it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('small', 'GCN', 1) | 0.9189±0.0000 | 0.9228±0.0000 | 0.0581±0.0000 | 0.0030±0.0000 |
Run
python train_dgl.py --dataset small --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
71%|██████████████████████████████████████████████████████████████████████████████████████████████████▋ | 710/1000 [00:17<00:06, 41.49it/s]Saving cascade info...
71%|███████████████████████████████████████████████████████████████████████████████████████████████████▏ | 714/1000 [00:27<00:11, 25.81it/s]
Optimization Finished!
Total time elapsed: 12.5899s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('small', 'GraphSAGE', 42) | 0.2208±0.0000 | 0.9236±0.0000 | 0.0805±0.0000 | 0.0530±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset small --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 92.361%
Teacher Parity SCORE: 0.08054204111876379
Teacher Equality SCORE: 0.05297064305684995
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [01:43<00:00, 5.81it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------------|---------------|---------------|---------------|---------------|
| ('small', 'GraphSAGE', 1) | 0.9209±0.0000 | 0.9218±0.0000 | 0.0589±0.0000 | 0.0025±0.0000 |
Run
python train_dgl.py --dataset small --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:23<00:00, 43.45it/s]
Optimization Finished!
Total time elapsed: 13.8847s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'GCN', 42) | 0.2497±0.0000 | 0.9222±0.0000 | 0.0676±0.0000 | 0.0182±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset small --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
Loading cascades...
60%|███████████████████████████████████████████████████████████████████████████▌ | 600/1000 [17:52<08:39, 1.30s/it]Stop!!!
60%|███████████████████████████████████████████████████████████████████████████▌ | 600/1000 [17:53<11:55, 1.79s/it]
Optimization Finished!
Total time elapsed: 957.3622s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'PLP', 42) | 0.9329±0.0000 | 0.9331±0.0000 | 0.0766±0.0000 | 0.0345±0.0000 |
Run
python train_dgl.py --dataset small --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
72%|████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 723/1000 [00:17<00:06, 41.83it/s]Saving cascade info...
72%|████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 723/1000 [00:27<00:10, 26.40it/s]
Optimization Finished!
Total time elapsed: 12.5953s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('small', 'GraphSAGE', 42) | 0.2195±0.0000 | 0.9235±0.0000 | 0.0812±0.0000 | 0.0612±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset small --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
Loading cascades...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [24:20<00:01, 1.25s/it]Stop!!!
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [24:21<00:01, 1.46s/it]
Optimization Finished!
Total time elapsed: 1271.5883s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'PLP', 42) | 0.9296±0.0000 | 0.9321±0.0000 | 0.0670±0.0000 | 0.0005±0.0000 |
Run
python train_dgl.py --dataset small --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:24<00:00, 41.41it/s]
Optimization Finished!
Total time elapsed: 14.2494s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('small', 'GCN', 42) | 0.2350±0.0000 | 0.9204±0.0000 | 0.0733±0.0000 | 0.0370±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset small --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 92.036%
Teacher Parity SCORE: 0.07330734697729868
Teacher Equality SCORE: 0.03696411929170551
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [01:22<00:00, 7.29it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('small', 'GCN', 1) | 0.9173±0.0000 | 0.9225±0.0000 | 0.0695±0.0000 | 0.0370±0.0000 |
Run
python train_dgl.py --dataset small --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 39424 nodes.
We have 144344 edges.
71%|██████████████████████████████████████████████████████████████████████████████████████████████████▋ | 710/1000 [00:17<00:06, 41.49it/s]Saving cascade info...
71%|███████████████████████████████████████████████████████████████████████████████████████████████████▏ | 714/1000 [00:27<00:11, 25.81it/s]
Optimization Finished!
Total time elapsed: 12.5899s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('small', 'GraphSAGE', 42) | 0.2208±0.0000 | 0.9236±0.0000 | 0.0805±0.0000 | 0.0530±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset small --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 92.361%
Teacher Parity SCORE: 0.08054204111876379
Teacher Equality SCORE: 0.05297064305684995
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [01:43<00:00, 5.81it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------------|---------------|---------------|---------------|---------------|
| ('small', 'GraphSAGE', 1) | 0.9226±0.0000 | 0.9231±0.0000 | 0.0775±0.0000 | 0.0485±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cpu
We have 129726 nodes.
We have 1311804 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [59:33<00:00, 3.57s/it]
Optimization Finished!
Total time elapsed: 2723.4194s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GCN', 42) | 0.1623±0.0000 | 0.9407±0.0000 | 0.0755±0.0000 | 0.0259±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset medium --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:4
We have 129726 nodes.
We have 1311804 edges.
Loading cascades...
94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 944/1000 [43:24<02:30, 2.68s/it]Stop!!!
94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 944/1000 [43:26<02:34, 2.76s/it]
Optimization Finished!
Total time elapsed: 1896.9747s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'PLP', 42) | 0.9451±0.0000 | 0.9467±0.0000 | 0.0334±0.0000 | 0.0710±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cpu
We have 129726 nodes.
We have 1311804 edges.
29%|██████████████████████████████████████▉ | 288/1000 [1:06:53<2:48:13, 14.18s/it] 29%|███████████████████████████████████████ | 289/1000 [1:07:07<2:48:08, 14.19s/it]
92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 922/1000 [3:41:03<18:57, 14.58s/it]Saving cascade info...
92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 922/1000 [3:42:18<18:48, 14.47s/it]
Optimization Finished!
Total time elapsed: 12489.5175s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GraphSAGE', 42) | 0.1618±0.0000 | 0.9409±0.0000 | 0.0789±0.0000 | 0.0372±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset medium --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:4
We have 129726 nodes.
We have 1311804 edges.
Loading cascades...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [45:37<00:02, 2.78s/it]Stop!!!
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [45:39<00:02, 2.74s/it]
Optimization Finished!
Total time elapsed: 2048.4245s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'PLP', 42) | 0.9397±0.0000 | 0.9411±0.0000 | 0.0349±0.0000 | 0.0693±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 129726 nodes.
We have 1311804 edges.
91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 907/1000 [00:46<00:04, 19.77it/s]Saving cascade info...
91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 907/1000 [01:45<00:10, 8.62it/s]
Optimization Finished!
Total time elapsed: 28.2527s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GCN', 42) | 0.1646±0.0000 | 0.9410±0.0000 | 0.0680±0.0000 | 0.0135±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset medium --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 94.102%
Teacher Parity SCORE: 0.06799369569420602
Teacher Equality SCORE: 0.013509012101442508
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [06:01<00:00, 1.66it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GCN', 1) | 0.9333±0.0000 | 0.9360±0.0000 | 0.0369±0.0000 | 0.0647±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 129726 nodes.
We have 1311804 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:59<00:00, 16.70it/s]
Optimization Finished!
Total time elapsed: 42.9725s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GraphSAGE', 42) | 0.1623±0.0000 | 0.9406±0.0000 | 0.0747±0.0000 | 0.0207±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset medium --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 94.065%
Teacher Parity SCORE: 0.074683525020946
Teacher Equality SCORE: 0.02070731895567679
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [08:09<00:00, 1.23it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GraphSAGE', 1) | 0.9363±0.0000 | 0.9389±0.0000 | 0.0320±0.0000 | 0.0805±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cpu
We have 129726 nodes.
We have 1311804 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [59:33<00:00, 3.57s/it]
Optimization Finished!
Total time elapsed: 2723.4194s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GCN', 42) | 0.1623±0.0000 | 0.9407±0.0000 | 0.0755±0.0000 | 0.0259±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset medium --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:4
We have 129726 nodes.
We have 1311804 edges.
Loading cascades...
80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 805/1000 [36:49<08:56, 2.75s/it]Stop!!!
80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 805/1000 [36:52<08:55, 2.75s/it]
Optimization Finished!
Total time elapsed: 1671.9693s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'PLP', 42) | 0.9470±0.0000 | 0.9476±0.0000 | 0.0672±0.0000 | 0.0221±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cpu
We have 129726 nodes.
We have 1311804 edges.
29%|██████████████████████████████████████▉ | 288/1000 [1:06:53<2:48:13, 14.18s/it] 29%|███████████████████████████████████████ | 289/1000 [1:07:07<2:48:08, 14.19s/it]
92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 922/1000 [3:41:03<18:57, 14.58s/it]Saving cascade info...
92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 922/1000 [3:42:18<18:48, 14.47s/it]
Optimization Finished!
Total time elapsed: 12489.5175s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GraphSAGE', 42) | 0.1618±0.0000 | 0.9409±0.0000 | 0.0789±0.0000 | 0.0372±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset medium --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:4
We have 129726 nodes.
We have 1311804 edges.
Loading cascades...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [45:37<00:02, 2.76s/it]Stop!!!
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [45:39<00:02, 2.74s/it]
Optimization Finished!
Total time elapsed: 2039.4652s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'PLP', 42) | 0.9429±0.0000 | 0.9432±0.0000 | 0.0724±0.0000 | 0.0180±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 129726 nodes.
We have 1311804 edges.
91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 907/1000 [00:46<00:04, 19.77it/s]Saving cascade info...
91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 907/1000 [01:45<00:10, 8.62it/s]
Optimization Finished!
Total time elapsed: 28.2527s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GCN', 42) | 0.1646±0.0000 | 0.9410±0.0000 | 0.0680±0.0000 | 0.0135±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset medium --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 94.102%
Teacher Parity SCORE: 0.06799369569420602
Teacher Equality SCORE: 0.013509012101442508
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [06:06<00:00, 1.64it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GCN', 1) | 0.9355±0.0000 | 0.9377±0.0000 | 0.0640±0.0000 | 0.0037±0.0000 |
Run
python train_dgl.py --dataset medium --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 129726 nodes.
We have 1311804 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:59<00:00, 16.70it/s]
Optimization Finished!
Total time elapsed: 42.9725s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GraphSAGE', 42) | 0.1623±0.0000 | 0.9406±0.0000 | 0.0747±0.0000 | 0.0207±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset medium --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 94.065%
Teacher Parity SCORE: 0.074683525020946
Teacher Equality SCORE: 0.02070731895567679
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [07:56<00:00, 1.26it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('medium', 'GraphSAGE', 1) | 0.9382±0.0000 | 0.9409±0.0000 | 0.0570±0.0000 | 0.0157±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: 0
We have 30000 nodes.
We have 2873716 edges.
50%|█████████████████████████████████████████████████████████████████████▌ | 500/1000 [00:08<00:07, 67.74it/s]Saving cascade info...
50%|█████████████████████████████████████████████████████████████████████▉ | 503/1000 [00:08<00:08, 59.42it/s]
Optimization Finished!
Total time elapsed: 5.4535s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GCN', 42) | 0.5387±0.0000 | 0.7739±0.0000 | 0.1332±0.0000 | 0.1040±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset credit --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
Loading cascades...
65%|██████████████████████████████████████████████████████████████████████████████████████████▉ | 654/1000 [03:03<01:46, 3.24it/s]Stop!!!
65%|██████████████████████████████████████████████████████████████████████████████████████████▉ | 654/1000 [03:04<01:37, 3.55it/s]
Optimization Finished!
Total time elapsed: 127.5057s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'PLP', 42) | 0.7809±0.0000 | 0.7964±0.0000 | 0.1167±0.0000 | 0.0718±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
50%|██████████████████████████████████████████████████████████████████████▏ | 505/1000 [00:29<00:27, 17.76it/s]Saving cascade info...
51%|██████████████████████████████████████████████████████████████████████▎ | 506/1000 [00:29<00:29, 16.91it/s]
Optimization Finished!
Total time elapsed: 21.6620s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GraphSAGE', 42) | 0.5290±0.0000 | 0.7809±0.0000 | 0.1419±0.0000 | 0.1149±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset credit --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
Loading cascades...
51%|██████████████████████████████████████████████████████████████████████▌ | 508/1000 [02:22<02:16, 3.62it/s]Stop!!!
51%|██████████████████████████████████████████████████████████████████████▌ | 508/1000 [02:22<02:17, 3.57it/s]
Optimization Finished!
Total time elapsed: 98.8265s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'PLP', 42) | 0.7795±0.0000 | 0.7823±0.0000 | 0.0252±0.0000 | 0.0168±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
50%|█████████████████████████████████████████████████████████████████████▋ | 501/1000 [00:15<00:10, 48.03it/s]Saving cascade info...
50%|█████████████████████████████████████████████████████████████████████▉ | 503/1000 [00:15<00:15, 32.76it/s]
Optimization Finished!
Total time elapsed: 9.7704s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GCN', 42) | 0.5387±0.0000 | 0.7739±0.0000 | 0.1332±0.0000 | 0.1040±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset credit --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Namespace(dataset='credit', teacher='GCN', metric='sp', dropout=0.5, gpu=0, lr=0.01, n_epochs=600, n_hidden=64, n_layers=1, weight_decay=0.0005, alpha=10.0, self_loop=True, labelrate=20, proxy=2, seed=0, role='stu', d_critic=1, g_critic=1, n_runs=1)
_config {'dataset_source': 'npz', 'seed': 0, 'train_config': {'split': {'train_examples_per_class': 20, 'val_examples_per_class': 30}, 'standardize_graph': True}}
Device: cuda:0
Teacher Test SCORE: 77.387%
Teacher Parity SCORE: 0.13319932602835127
Teacher Equality SCORE: 0.10404228804766014
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [01:20<00:00, 7.42it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GCN', 1) | 0.7781±0.0000 | 0.7915±0.0000 | 0.0259±0.0000 | 0.0243±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
50%|██████████████████████████████████████████████████████████████████████▏ | 505/1000 [00:11<00:10, 48.00it/s]Saving cascade info...
51%|██████████████████████████████████████████████████████████████████████▎ | 506/1000 [00:11<00:11, 43.90it/s]
Optimization Finished!
Total time elapsed: 7.5217s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GraphSAGE', 42) | 0.5290±0.0000 | 0.7809±0.0000 | 0.1419±0.0000 | 0.1149±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset credit --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 78.093%
Teacher Parity SCORE: 0.14190014062030198
Teacher Equality SCORE: 0.11489623517791714
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [04:59<00:00, 2.00it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GraphSAGE', 1) | 0.7807±0.0000 | 0.7909±0.0000 | 0.0116±0.0000 | 0.0095±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: 0
We have 30000 nodes.
We have 2873716 edges.
50%|█████████████████████████████████████████████████████████████████████▌ | 500/1000 [00:08<00:07, 67.74it/s]Saving cascade info...
50%|█████████████████████████████████████████████████████████████████████▉ | 503/1000 [00:08<00:08, 59.42it/s]
Optimization Finished!
Total time elapsed: 5.4535s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GCN', 42) | 0.5387±0.0000 | 0.7739±0.0000 | 0.1332±0.0000 | 0.1040±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset credit --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
Loading cascades...
60%|████████████████████████████████████████████████████████████████████████████ | 604/1000 [04:28<02:49, 2.33it/s]Stop!!!
60%|████████████████████████████████████████████████████████████████████████████ | 604/1000 [04:28<02:56, 2.25it/s]
Optimization Finished!
Total time elapsed: 210.4350s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'PLP', 42) | 0.7797±0.0000 | 0.7963±0.0000 | 0.1275±0.0000 | 0.0783±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
50%|██████████████████████████████████████████████████████████████████████▏ | 505/1000 [00:29<00:27, 17.76it/s]Saving cascade info...
51%|██████████████████████████████████████████████████████████████████████▎ | 506/1000 [00:29<00:29, 16.91it/s]
Optimization Finished!
Total time elapsed: 21.6620s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GraphSAGE', 42) | 0.5290±0.0000 | 0.7809±0.0000 | 0.1419±0.0000 | 0.1149±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset credit --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
Loading cascades...
99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏| 994/1000 [15:21<00:05, 1.19it/s]Stop!!!
99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏| 994/1000 [15:22<00:05, 1.08it/s]
Optimization Finished!
Total time elapsed: 759.5070s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'PLP', 42) | 0.7831±0.0000 | 0.7928±0.0000 | 0.0652±0.0000 | 0.0477±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
50%|█████████████████████████████████████████████████████████████████████▋ | 501/1000 [00:15<00:10, 48.03it/s]Saving cascade info...
50%|█████████████████████████████████████████████████████████████████████▉ | 503/1000 [00:15<00:15, 32.76it/s]
Optimization Finished!
Total time elapsed: 9.7704s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GCN', 42) | 0.5387±0.0000 | 0.7739±0.0000 | 0.1332±0.0000 | 0.1040±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset credit --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 77.387%
Teacher Parity SCORE: 0.13319932602835127
Teacher Equality SCORE: 0.10404228804766014
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [01:33<00:00, 6.41it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GCN', 1) | 0.7776±0.0000 | 0.7908±0.0000 | 0.0726±0.0000 | 0.0560±0.0000 |
Run
python train_dgl.py --dataset credit --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 30000 nodes.
We have 2873716 edges.
50%|██████████████████████████████████████████████████████████████████████▏ | 505/1000 [00:11<00:10, 48.00it/s]Saving cascade info...
51%|██████████████████████████████████████████████████████████████████████▎ | 506/1000 [00:11<00:11, 43.90it/s]
Optimization Finished!
Total time elapsed: 7.5217s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|-----------------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GraphSAGE', 42) | 0.5290±0.0000 | 0.7809±0.0000 | 0.1419±0.0000 | 0.1149±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset credit --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 78.093%
Teacher Parity SCORE: 0.14190014062030198
Teacher Equality SCORE: 0.11489623517791714
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [04:58<00:00, 2.01it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|----------------------------|---------------|---------------|---------------|---------------|
| ('credit', 'GraphSAGE', 1) | 0.7792±0.0000 | 0.7893±0.0000 | 0.0753±0.0000 | 0.0565±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 92.06it/s]
Optimization Finished!
Total time elapsed: 6.8720s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GCN', 42) | 0.2577±0.0000 | 0.9225±0.0000 | 0.0619±0.0000 | 0.0225±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset bail --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
Loading cascades...
78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 776/1000 [03:14<00:55, 4.02it/s]Stop!!!
78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 776/1000 [03:14<00:56, 3.99it/s]
Optimization Finished!
Total time elapsed: 131.7941s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'PLP', 42) | 0.8928±0.0000 | 0.8994±0.0000 | 0.0567±0.0000 | 0.0229±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:38<00:00, 25.98it/s]
Optimization Finished!
Total time elapsed: 27.4833s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GraphSAGE', 42) | 0.3154±0.0000 | 0.9145±0.0000 | 0.0697±0.0000 | 0.0285±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset bail --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:3
We have 18876 nodes.
We have 642616 edges.
Loading cascades...
51%|███████████████████████████████████████████████████████████████████████▏ | 512/1000 [02:09<02:01, 4.03it/s]Stop!!!
51%|███████████████████████████████████████████████████████████████████████▏ | 512/1000 [02:10<02:03, 3.94it/s]
Optimization Finished!
Total time elapsed: 87.6705s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'PLP', 42) | 0.8910±0.0000 | 0.8986±0.0000 | 0.0552±0.0000 | 0.0173±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:13<00:00, 76.24it/s]
Optimization Finished!
Total time elapsed: 8.5631s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GCN', 42) | 0.2594±0.0000 | 0.9231±0.0000 | 0.0618±0.0000 | 0.0193±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset bail --teacher GCN --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 92.308%
Teacher Parity SCORE: 0.06178673506462984
Teacher Equality SCORE: 0.019254548666313376
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [02:13<00:00, 4.50it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|--------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GCN', 1) | 0.8899±0.0000 | 0.8951±0.0000 | 0.0553±0.0000 | 0.0277±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:12<00:00, 81.56it/s]
Optimization Finished!
Total time elapsed: 8.2781s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GraphSAGE', 42) | 0.3081±0.0000 | 0.9147±0.0000 | 0.0679±0.0000 | 0.0312±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset bail --teacher GraphSAGE --metric sp
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 91.471%
Teacher Parity SCORE: 0.06787802435114115
Teacher Equality SCORE: 0.031160572337042947
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [02:18<00:00, 4.34it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|--------------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GraphSAGE', 1) | 0.8978±0.0000 | 0.9012±0.0000 | 0.0649±0.0000 | 0.0350±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GCN --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 92.06it/s]
Optimization Finished!
Total time elapsed: 6.8720s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GCN', 42) | 0.2577±0.0000 | 0.9225±0.0000 | 0.0619±0.0000 | 0.0225±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset bail --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
Loading cascades...
55%|████████████████████████████████████████████████████████████████████████████▏ | 548/1000 [02:17<01:52, 4.03it/s]Stop!!!
55%|████████████████████████████████████████████████████████████████████████████▏ | 548/1000 [02:18<01:53, 3.97it/s]
Optimization Finished!
Total time elapsed: 92.3854s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'PLP', 42) | 0.8663±0.0000 | 0.8710±0.0000 | 0.0442±0.0000 | 0.0072±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GraphSAGE --framework CPF
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:38<00:00, 25.98it/s]
Optimization Finished!
Total time elapsed: 27.4833s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GraphSAGE', 42) | 0.3154±0.0000 | 0.9145±0.0000 | 0.0697±0.0000 | 0.0285±0.0000 |
After obtaining the teacher knowledge, then run
python spawn_worker.py --dataset bail --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:3
We have 18876 nodes.
We have 642616 edges.
Loading cascades...
51%|██████████████████████████████████████████████████████████████████████▉ | 510/1000 [02:08<02:00, 4.06it/s]Stop!!!
51%|██████████████████████████████████████████████████████████████████████▉ | 510/1000 [02:08<02:03, 3.97it/s]
Optimization Finished!
Total time elapsed: 87.0457s
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'PLP', 42) | 0.8901±0.0000 | 0.8986±0.0000 | 0.0625±0.0000 | 0.0257±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GCN --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:13<00:00, 76.24it/s]
Optimization Finished!
Total time elapsed: 8.5631s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GCN', 42) | 0.2594±0.0000 | 0.9231±0.0000 | 0.0618±0.0000 | 0.0193±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset bail --teacher GCN --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 92.308%
Teacher Parity SCORE: 0.06178673506462984
Teacher Equality SCORE: 0.019254548666313376
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [02:20<00:00, 4.26it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|--------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GCN', 1) | 0.8904±0.0000 | 0.8970±0.0000 | 0.0553±0.0000 | 0.0276±0.0000 |
Run
python train_dgl.py --dataset bail --teacher GraphSAGE --framework GraphAKD
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
We have 18876 nodes.
We have 642616 edges.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:12<00:00, 81.56it/s]
Optimization Finished!
Total time elapsed: 8.2781s
| Variant | TestLoss | TestAcc | DeltaSP | DeltaEO |
|---------------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GraphSAGE', 42) | 0.3081±0.0000 | 0.9147±0.0000 | 0.0679±0.0000 | 0.0312±0.0000 |
After obtaining the teacher knowledge, then we enter the directory
cd ../GraphAKD/GraphAKD/GraphAKD-main/node-level/stu-gcn
and run
python train.py --dataset bail --teacher GraphSAGE --metric eo
Based on a fixed seed, we present the sample log as follows.
Device: cuda:0
Teacher Test SCORE: 91.471%
Teacher Parity SCORE: 0.06787802435114115
Teacher Equality SCORE: 0.031160572337042947
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 600/600 [02:27<00:00, 4.07it/s]
Final results:
| Variant | TestAcc | ValAcc | DeltaSP | DeltaEO |
|--------------------------|---------------|---------------|---------------|---------------|
| ('bail', 'GraphSAGE', 1) | 0.8965±0.0000 | 0.8986±0.0000 | 0.0565±0.0000 | 0.0317±0.0000 |