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https://github.com/rootlu/L2P-GNN
Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"
https://github.com/rootlu/L2P-GNN
aaai2021 gnn meta-learning pre-train
Last synced: 14 days ago
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Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"
- Host: GitHub
- URL: https://github.com/rootlu/L2P-GNN
- Owner: rootlu
- Created: 2020-12-03T08:07:21.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-02-27T05:56:52.000Z (over 3 years ago)
- Last Synced: 2023-03-04T12:34:41.910Z (over 1 year ago)
- Topics: aaai2021, gnn, meta-learning, pre-train
- Language: Python
- Homepage:
- Size: 19.7 MB
- Stars: 79
- Watchers: 2
- Forks: 15
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Lists
- StarryDivineSky - rootlu/L2P-GNN
README
# Learning to Pre-train Graph Neural Networks
This repository is the official implementation of AAAI-2021 paper [Learning to Pre-train Graph Neural Networks](https://yuanfulu.github.io/publication/AAAI-L2PGNN.pdf)
## Requirements
To install requirements:
```setup
pip install -r requirements.txt
```## Dataset
All the necessary data files can be downloaded from the following links.
For Biology dataset, download from [Google Drive](https://drive.google.com/drive/folders/18vpBvSajIrme2xsbx8Oq8aTWIWMlSgJT?usp=sharing) and [BaiduYun](https://pan.baidu.com/s/1Yv6dN7F1jgTSz9-nU1eN-A) (Extraction code: j97n), unzip it, and put the under `data/bio/`.
The new compilation of bibliographic graphs, i.e., PreDBLP, download from [Google Drive](https://drive.google.com/drive/folders/18vpBvSajIrme2xsbx8Oq8aTWIWMlSgJT?usp=sharing) and [BaiduYun](https://pan.baidu.com/s/1Yv6dN7F1jgTSz9-nU1eN-A) (Extraction code: j97n), unzip it, and move the `dblp.graph` file to `data/dblp/unsupervised/processed/` and the `dblpfinetune.graph` file to `data/dblp/supervised/processed/`, respectively.
Also, to avoid the "file incomplete" errors caused by compressed files, we also upload the uncompressed dblp dataset at [BaiduYun](https://pan.baidu.com/s/1Yv6dN7F1jgTSz9-nU1eN-A) (Extraction code: j97n).
## Training
To pre-train L2P-GNN on Biology dataset w.r.t. GIN model, run this command:
```shell
python main.py --dataset DATASET --gnn_type GNN_MODEL --model_file PRE_TRAINED_MODEL_NAME --device 1
```The pre-trained models are saved into `res/DATASET/` .
## Evaluation
To fine-tune L2P-GNN on Biology dataset, run:
```shell
python eval_bio.py --dataset DATASET --gnn_type GNN_MODEL --emb_trained_model_file EMB_TRAINED_FILE --pre_trained_model_file GNN_TRAINED_FILE --pool_trained_model_file POOL_TRAINED_FILE --result_file RESULT_FILE --device 1
```The results w.r.t 10 random running seeds are saved into `res/DATASET/finetune_seed(0-9)/`
## Results
To analysis results of downstream tasks, run:
```shell
python result_analysis.py --dataset DATASET --times SEED_NUM
```where `SEED_NUM` is the number of random seed ranging from 0 to 9, thus it is usually set to 10.
## Reproducing results in the paper
Our results in the paper can be reproduced by directly running:
```shell
python eval_bio.py --dataset bio --gnn_type gin --emb_trained_model_file co_adaptation_5_300_gin_50_emb.pth --pre_trained_model_file co_adaptation_5_300_gin_50_gnn.pth --pool_trained_model_file co_adaptation_5_300_gin_50_pool.pth --result_file co_adaptation_5_300_gin_50 --device 0
```and
```shell
python eval_dblp.py --dataset dblp --gnn_type gin --split random --emb_trained_model_file co_adaptation_5_300_s50q30_gin_20_emb.pth --pre_trained_model_file co_adaptation_5_300_s50q30_gin_20_gnn.pth --pool_trained_model_file co_adaptation_5_300_s50q30_gin_20_pool.pth --result_file co_adaptation_5_300_s50q30_gin_20 --device 0 --dropout_ratio 0.1
```