## Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification
## Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification (UniMP)
This experiment is based on stanford OGB (1.2.1) benchmark. The description of 《Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification》 is [avaiable here](). The steps are:
This experiment is based on stanford OGB (1.2.1) benchmark. The description of 《Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification》 is [avaiable here](https://arxiv.org/pdf/2009.03509.pdf). The steps are:
### Install environment:
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@@ -12,14 +12,14 @@ This experiment is based on stanford OGB (1.2.1) benchmark. The description of
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### Arxiv dataset:
1.```python main_arxiv.py --place 0 --log_file arxiv_baseline.txt``` to get the baseline result of arxiv dataset.
2.```python main_arxiv.py --place 0 --use_label_e --log_file arxiv_unipm.txt``` to get the UniPM result of arxiv dataset.
2.```python main_arxiv.py --place 0 --use_label_e --log_file arxiv_unimp.txt``` to get the UniMP result of arxiv dataset.
### Products dataset:
1.```python main_product.py --place 0 --log_file product_label_embedding.txt --use_label_e``` to get the UniPM result of Products dataset.
1.```python main_product.py --place 0 --log_file product_unimp.txt --use_label_e``` to get the UniMP result of Products dataset.
### Proteins dataset:
1.```python main_protein.py --place 0 --log_file protein_baseline.txt ``` to get the baseline result of Proteins dataset.
2.```python main_protein.py --place 0 --use_label_e --log_file protein_label_embedding.txt``` to get the UniPM result of Proteins dataset.
2.```python main_protein.py --place 0 --use_label_e --log_file protein_unimp.txt``` to get the UniMP result of Proteins dataset.
