https://github.com/oniani/cancer_research_gnn
https://github.com/oniani/cancer_research_gnn
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/oniani/cancer_research_gnn
- Owner: oniani
- Created: 2020-07-22T17:20:45.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-09T00:50:43.000Z (almost 6 years ago)
- Last Synced: 2025-07-05T11:51:42.459Z (about 1 year ago)
- Language: Python
- Size: 135 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Citation: citation_network/README.md
Awesome Lists containing this project
README
# Results
- [GraphSAGE](#graphsage)
- [Mean Aggregator](#mean-aggregator)
- [GCN Aggregator](#gcn-aggregator)
- [MoNet](#monet)
- [GAT](#gat)
- [GCN](#gcn)
- [APPNP](#appnp)
- [GIN](#gin)
- [TAGCN](#tagcn)
- [SGC](#sgc)
- [AGNN](#agnn)
- [ChebNet](#chebnet)
## GraphSAGE
### Mean Aggregator
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.9012345679012346 |
| Precision | 0.9261904761904762 |
| Recall | 0.9102607709750566 |
| F-Score | 0.9147043432757718 |
| Hyperparameter | Value |
| --------------------------- | ------------- |
| Dropout probability | 0.25 |
| Learning rate | 1e-2 (0.01) |
| Number of training epochs | 800 |
| Number of hidden gcn units | 16 |
| Number of hidden gcn layers | 1 |
| Weight for L2 loss | 5e-4 (0.0005) |
| Aggregator type | mean |
### GCN Aggregator
StatisticsHyperparameters
| Statistic | Value |
| --------- | -------------------- |
| Accuracy | 0.2222222222222222 |
| Precision | 0.031746031746031744 |
| Recall | 0.14285714285714285 |
| F-Score | 0.051948051948051945 |
| Hyperparameter | Value |
| --------------------------- | ------------- |
| Dropout probability | 0.25 |
| Learning rate | 1e-1 (0.1) |
| Number of training epochs | 800 |
| Number of hidden gcn units | 2 |
| Number of hidden gcn layers | 1 |
| Weight for L2 loss | 5e-4 (0.0005) |
| Aggregator type | gcn |
## MoNet
StatisticsHyperparameters
| Statistic | Value |
| --------- | -------------------- |
| Accuracy | 0.2222222222222222 |
| Precision | 0.031746031746031744 |
| Recall | 0.14285714285714285 |
| F-Score | 0.051948051948051945 |
| Hyperparameter | Value |
| -------------------------------------------------------------------- | ------------- |
| Dropout probability | 0.25 |
| Learning rate | 1e-1 (0.1) |
| Number of training epochs | 800 |
| Number of hidden gcn units | 2 |
| Number of hidden gcn layers | 1 |
| Pseudo coordinate dimensions in GMMConv, 2 for cora and 3 for pubmed | 2 |
| Number of kernels in GMMConv layer | 3 |
| Weight for L2 loss | 5e-4 (0.0005) |
## GAT
StatisticsHyperparameters
| Statistic | Value |
| --------- | ----- |
| Accuracy | 0.753 |
| Precision | 0.726 |
| Recall | 0.684 |
| F-Score | 0.697 |
| Hyperparameter | Value |
| ------------------------------------------ | ----------- |
| Number of training epochs | 1000 |
| Number of hidden attention heads | 4 |
| Uumber of output attention heads | 1 |
| Number of hidden layers | 1 |
| Number of hidden units | 200 |
| Use residual connection | False |
| Input feature dropout | 0 |
| Attention dropout | 0 |
| Learning rate | 1e-2 (0.01) |
| Weight decay | 0 |
| The negative slope of leaky relu | 0.2 |
| Indicates whether to use early stop or not | False |
| Skip re-evaluate the validation set | False |
## GCN
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.8024691358024691 |
| Precision | 0.8550170307357392 |
| Recall | 0.786734693877551 |
| F-Score | 0.8035058227176454 |
| Hyperparameter | Value |
| --------------------------- | ----------- |
| Dropout probability | 0 |
| Learning rate | 1e-2 (0.01) |
| Number of training epochs | 4000 |
| Number of hidden gcn units | 500 |
| Number of hidden gcn layers | 1 |
| Weight for L2 loss | 0 |
## APPNP
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.8888888888888888 |
| Precision | 0.9251082251082252 |
| Recall | 0.8943877551020407 |
| F-Score | 0.9049666689418242 |
| Hyperparameter | Value |
| --------------------------- | ------------- |
| Input feature dropout | 0.25 |
| Edge propagation dropout | 0.5 |
| Learning rate | 1e-1 (0.1) |
| Number of training epochs | 800 |
| Hidden unit sizes for appnp | [64] |
| Number of propagation steps | 10 |
| Teleport Probability | 0.4 |
| Weight for L2 loss | 5e-4 (0.0005) |
## GIN
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.8271604938271605 |
| Precision | 0.8205627705627706 |
| Recall | 0.8091836734693878 |
| F-Score | 0.8045525902668759 |
| Hyperparameter | Value |
| ------------------------- | ---------------- |
| Extra args | [16, 1, 0, True] |
| Learning rate | 1e-2 (0.01) |
| Weight decay | 5e-6 (0.000005) |
| Number of training epochs | 800 |
## TAGCN
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.9012345679012346 |
| Precision | 0.9070381998953428 |
| Recall | 0.9102607709750566 |
| F-Score | 0.9041060526774812 |
| Hyperparameter | Value |
| ------------------------- | -------------------- |
| Extra args | [16, 1, F.relu, 0.5] |
| Learning rate | 1e-2 (0.01) |
| Weight decay | 5e-4 (0.0005) |
| Number of training epochs | 800 |
## SGC
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.8271604938271605 |
| Precision | 0.8362389490209041 |
| Recall | 0.8315759637188209 |
| F-Score | 0.8240298807695121 |
| Hyperparameter | Value |
| ------------------------- | ---------------- |
| Extra args | [None, 1, False] |
| Learning rate | 1e-1 (0.1) |
| Weight decay | 0 |
| Number of training epochs | 4000 |
## AGNN
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.8765432098765432 |
| Precision | 0.88992673992674 |
| Recall | 0.8888321995464853 |
| F-Score | 0.8850179383028748 |
| Hyperparameter | Value |
| ------------------------- | ------------------------ |
| Extra args | [100, 1, 1.0, True, 0.1] |
| Learning rate | 1e-1 (0.1) |
| Weight decay | 0 |
| Number of training epochs | 200 |
## ChebNet
StatisticsHyperparameters
| Statistic | Value |
| --------- | ------------------ |
| Accuracy | 0.9012345679012346 |
| Precision | 0.9022735409953455 |
| Recall | 0.9201814058956915 |
| F-Score | 0.9073651359365645 |
| Hyperparameter | Value |
| ------------------------- | ---------------- |
| Extra args | [32, 1, 2, True] |
| Learning rate | 1e-2 (0.001) |
| Weight decay | 5e-4 (0.0005) |
| Number of training epochs | 800 |
## Feature Engineering
```sh
python generate_data.py --num_classes=6
python feature_engineering.py
```