**Note**：The test data labels are randomly replaced to prevent data leakage issues, refer to [HGB](https://github.com/THUDM/HGB).
In OpenHGNN, you will get the test results in `./openhgnn/output/{model_name}/`. If you want to obtain test scores, you need to submit your prediction to HGB's [website](https://www.biendata.xyz/hgb/).
- HGBn-ACM
| paper | author | subject | term | paper-author | paper-paper | paper-subject | paper-term | Val | Test |
@@ -86,10 +90,14 @@ So dataset should load not only a heterograph[DGLGraph], but also some index inv
- ###### academic4HetGNN
- **HGBl-LinkPrediction**
- **HGBl**
The datasets are HGB for Link Prediction.
**Note**：The test data labels are randomly replaced to prevent data leakage issues, refer to [HGB](https://github.com/THUDM/HGB).
In OpenHGNN, you will get the test results in `./openhgnn/output/{model_name}/`. If you want to obtain test scores, you need to submit your prediction to HGB's [website](https://www.biendata.xyz/hgb/).
- HGBl-amazon
| | product | features | product-product0 | product-product1 | test : product-product0 | test : product-product1 |
@@ -12,7 +12,7 @@ The installation process is same with OpenHGNN [Get Started](https://github.com/
#### 2.1 Generate designs randomly
Here we will generate a random design combination for each dataset and save it in a `.yaml` file. The candidate designs are list in [`./space4hgnn/generate_yaml.py`](./generate_yaml.py).
Here we will generate a random design combination for each dataset and save it in a `.yaml` file. The candidate designs are listed in [`./space4hgnn/generate_yaml.py`](./generate_yaml.py).
```bash
python ./space4hgnn/generate_yaml.py --gnn_type gcnconv --times 1 --key has_bn --configfile test
@@ -85,7 +85,9 @@ For **Meta-path model family**, ``--model`` is general_HGNN and ``--subgraph_ext
**Note: ** Similar with generating yaml file, experiment will load the design configuration from ``yaml_file_path``. And it will save the results into a `.csv` file in `prediction_file_path`.
**Note: **
Similar with generating yaml file, experiment will load the design configuration from ``yaml_file_path``. And it will save the results into a `.csv` file in `prediction_file_path`.
We analyze the results with average ranking following [GraphGym](https://github.com/snap-stanford/GraphGym#3-analyze-the-results), and
We analyze the results with average ranking following [GraphGym](https://github.com/snap-stanford/GraphGym#3-analyze-the-results), the according codeis in [`figure/rank.py`](./figure/rank.py).
We analyze the results with distribution estimates following [NDS](https://github.com/facebookresearch/nds), and
We analyze the results with distribution estimates following [NDS](https://github.com/facebookresearch/nds), and the according code is in [`figure/distribution.py`](./figure/distribution.py).
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