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Bridge-Tune

About

This repo is the official code for AAAI-24 "Measuring Task Similarity and Its Implication in Fine-Tuning Graph Neural Networks"

Dependencies

The script has been tested running under Python 3.7.10, with the following packages installed (along with their dependencies):

  • PyTorch. Version >=1.4 required. You can find instructions to install from source here.

  • DGL. 0.5 > Version >=0.4.3 required. You can find instructions to install from source here.

  • rdkit. Version = 2019.09.2 required. It can be easily installed with conda install -c conda-forge rdkit=2019.09.2

  • Other Python modules. Some other Python module dependencies are listed in requirements.txt, which can be easily installed with pip:

    pip install -r requirements.txt

In addition, CUDA 10.0 has been used in our project. Although not all dependencies are mentioned in the installation instruction links above, you can find most of the libraries in the package repository of a regular Linux distribution.

File folders

data: contains the data of "DD242, DD68, DD687, usa_airport, brazil_airport, europe_airport".

splits: need to unzipped, contains the split data of "cora, pubmed, cornell and wisconsin".

scripts: contains all the scripts for running code.

gcc&utils: contains the code of models.

saved: contains the pre-trained graph obtained from GCC. The specific path for pre-trained model is located in saved/Pretrain_moco_True_dgl_gin_layer_5_lr_0.005_decay_1e-05_bsz_32_hid_64_samples_2000_nce_t_0.07_nce_k_16384_rw_hops_256_restart_prob_0.8_aug_1st_ft_False_deg_16_pos_32_momentum_0.999/

Usage: How to run the code

We divide it into two steps (1) Fine-tuning (2) Evaluating the performance fine-tuned model.

1. Fine-tuning via Bridge-Tune

python train_bridge.py \
  --resume <pre-trained model file> \
  --dataset <downstream dataset>
  --reg-coeff <coefficient for Bridge-Tune Loss> \
  --model-path <fine-tuned model saved file> \
  --gpu <gpu id> \
  --epochs <epoch number> \
  --bridge

For more detail, the help information of the main script train_bridge.py can be obtain by executing the following command.

python train_bridge.py -h

optional arguments:
  -h, --help            show this help message and exit
  --epochs EPOCHS       number of training epochs (default:30)
  --optimizer {sgd,adam,adagrad}
                        optimizer (default:adam)
  --learning_rate LEARNING_RATE  learning rate (default:0.005)
  --resume PATH         path for pre-trained model (default: GCC)
  --dataset {usa_airport,brazil_airport,europe_airport,h-index, texas,DD242,cornell,wisconsin,citeseer}
  --reg-coeff REG_COEFF  coefficient for Bridge-Tune (default:10)
  --hidden-size HIDDEN_SIZE  (default:64)
  --model-path MODEL_PATH    path to save fine-tuned model (default:saved)
  --finetune            whether to conduct fine-tune
  --gpu GPU [GPU ...]   GPU id to use.
  --bridge              whether to conduct bridge-tune

Demo:

python train_bridge.py \
  --dataset usa_airport \
  --reg-coeff 10 \
  --model-path saved \
  --gpu 0 \
  --epochs 50 \
  --bridge

Note: No need to specify the pre-trained model path here; the default pre-trained model is provided. If a user requires a specific model, they should add the --resume.

2. Evaluating

generate.py file helps generate embeddings on a specific dataset. The help information of the main script generate.py can be obtain by executing the following command.

python generate.py -h

optional arguments:
  --load-path LOAD_PATH
  --dataset Dataset
  --gpu GPU  GPU id to use.

The embedding will be used for evaluation in node classification. The script evaluate.sh are available to simplify the evaluation process as follows:

bash evaluate.sh <model_path> <name> <dataset> <gpu id>

Here, <saved_path> refers to the main directory for finetuning, and <name> is the name of specific model directory.

Demo: Here is the demo instruction, after the user has trained using the demo provided above.

bash scripts/evaluate.sh saved path_bridge10_usa_airport usa_airport 0

Acknowledgements

Part of this code is inspired by Qiu et al.'s GCC: Graph Contrastive Coding.

Contact

If you have any question about the code or the paper, feel free to contact me. Email: [email protected]

Cite

If you find this work helpful, please cite

@article{huang2024measuring,
  title={Measuring Task Similarity and Its Implication in Fine-Tuning Graph Neural Networks},
  author={Huang, Renhong and Xu, Jiarong and Jiang, Xin and Pan, Chenglu and Yang, Zhiming and Wang, Chunping and Yang, Yang},
  booktitle={AAAI},
  volume={38},
  number={11}, 
  pages={12617-12625},
  year={2024}
}