Awesome
GPF
This is a Pytorch code Implementation of the paper Universal Prompt Tuning for Graph Neural Networks, which is accepted by the NeurIPS 2023. We provide two graph prompt methods GPF and GPF-plus to perform prompt tuning during the downstream adaptations.
Installation
We used the following packages under Python 3.7.
pytorch 1.4.0
torch-cluster 1.5.2
torch-geometric 1.0.3
torch-scatter 2.0.3
torch-sparse 0.5.1
torch-spline-conv 1.2.0
rdkit 2022.3.4
tqdm 4.31.1
tensorboardX 1.6
Pre-trained models
The pre-tiraned models we use follow the training steps of the paper Strategies for Pre-training Graph Neural Networks and Graph Contrastive Learning with Augmentations. For each pre-training dataset, we provide five basic pre-trained models, and the brief descriptions of their training strategies are as follows:
- Deep Graph Infomax (denoted by Infomax) It obtains expressive representations for graphs or nodes via maximizing the mutual information between graph-level representations and substructure-level represen- tations of different granularity.
- Edge Prediction (denoted by EdgePred) It is a regular graph reconstruction task used by many models, such as GAE. The prediction target is the existence of edge between a pair of nodes.
- Attribute Masking (denoted by AttrMasking) It masks node/edge attributes and then let GNNs predict those attributes based on neighboring structure.
- Context Prediction (denoted by ContextPred) It uses subgraphs to predict their surrounding graph structures, and aims to mapping nodes appearing in similar structural contexts to nearby embeddings.
- Graph Contrastive Learning (denoted by GCL) It embeds augmented versions of the anchor close to each other (positive samples) and pushes the embeddings of other samples (negatives) apart. We use the augmentation strategies proposed in Graph Contrastive Learning with Augmentations for generating the positive and negative samples.
Dataset
The pre-training and downstream datasets used in our experiments are referred to the paper Strategies for Pre-training Graph Neural Networks. You can download the biology and chemistry datasets from their repository.
To run the codes successfully, the downloaded datasets should be placed in /dataset under bio/ and chem/.
Downstream adaptation
Biology dataset
For the biology dataset, please run prompt_tuning_full_shot.py and prompt_tuning_few_shot.py under bio/ for downstream adaptations.
For the normal full-shot scenarios:
usage: prompt_tuning_full_shot.py [-h] [--device DEVICE]
[--epochs EPOCHS] [--lr LR] [--decay DECAY]
[--num_layer NUM_LAYER] [--emb_dim EMB_DIM]
[--dropout_ratio DROPOUT_RATIO]
[--model_file MODEL_FILE]
[--tuning_type TUNING_TYPE]
[--seed SEED] [--runseed RUNSEED]
[--num_layers NUM_LAYERS] [--pnum PNUM]
optional arguments:
--device Which gpu to use if any (default: 0)
--epochs Number of epochs to train (default: 50)
--lr Learning rate (default: 0.0001)
--decay Weight decay (default: 0)
--num_layer Number of GNN message passing layers (default: 5).
--emb_dim Embedding dimensions (default: 300)
--dropout_ratio Dropout ratio (default: 0.5)
--model_file File path to read the model (if there is any)
--tuning_type 'gpf' for GPF and 'gpf-plus' for GPF-plus in the paper
--seed Seed for splitting dataset.
--runseed Seed for running experiments.
--num_layers A range of [1,2,3]-layer MLPs with equal width
--pnum The number of independent basis for GPF-plus
For the few-show scenarios:
usage: prompt_tuning_few_shot.py [-h] [--device DEVICE]
[--epochs EPOCHS] [--lr LR] [--decay DECAY]
[--num_layer NUM_LAYER] [--emb_dim EMB_DIM]
[--dropout_ratio DROPOUT_RATIO]
[--model_file MODEL_FILE]
[--tuning_type TUNING_TYPE] [--seed SEED]
[--runseed RUNSEED]
[--num_layers NUM_LAYERS] [--pnum PNUM]
[--shot_number SHOT_NUMBER]
optional arguments:
--device Which gpu to use if any (default: 0)
--epochs Number of epochs to train (default: 50)
--lr Learning rate (default: 0.001)
--decay Weight decay (default: 0)
--num_layer Number of GNN message passing layers (default: 5).
--emb_dim Embedding dimensions (default: 300)
--dropout_ratio Dropout ratio (default: 0.5)
--model_file File path to read the model (if there is any)
--tuning_type 'gpf' for GPF and 'gpf-plus' for GPF-plus in the paper
--seed Seed for splitting dataset.
--runseed Seed for running experiments.
--num_layers A range of [1,2,3]-layer MLPs with equal width
--pnum The number of independent basis for GPF-plus
--shot_number Number of shots
Chemistry dataset
For the chemistry dataset, please run prompt_tuning_few_shot.py and prompt_tuning_full_shot.py under chem/ for downstream adaptations.
For the normal full-shot scenarios:
usage: prompt_tuning_full_shot.py [-h] [--device DEVICE]
[--epochs EPOCHS] [--lr LR] [--lr_scale LR_SCALE]
[--decay DECAY] [--num_layer NUM_LAYER]
[--emb_dim EMB_DIM] [--dropout_ratio DROPOUT_RATIO]
[--tuning_type TUNING_TYPE]
[--dataset DATASET] [--model_file MODEL_FILE]
[--seed SEED] [--runseed RUNSEED]
[--num_layers NUM_LAYERS] [--pnum PNUM]
optional arguments:
--device Which gpu to use if any (default: 0)
--epochs Number of epochs to train (default: 100)
--lr Learning rate (default: 0.001)
--lr_scale Relative learning rate for the feature extraction layer (default: 1)
--decay Weight decay (default: 0)
--num_layer Number of GNN message passing layers (default: 5).
--emb_dim Embedding dimensions (default: 300)
--dropout_ratio Dropout ratio (default: 0.5)
--tuning_type 'gpf' for GPF and 'gpf-plus' for GPF-plus in the paper
--dataset Root directory of dataset. For now, only classification.
--model_file File path to read the model (if there is any)
--seed Seed for splitting the dataset.
--runseed Seed for minibatch selection, random initialization.
--split The way of dataset split(e.g., 'scaffold' for chem data)
--num_layers A range of [1,2,3]-layer MLPs with equal width
--pnum The number of independent basis for GPF-plus
For the few-show scenarios:
usage: prompt_tuning_few_shot.py [-h] [--device DEVICE]
[--epochs EPOCHS] [--lr LR] [--lr_scale LR_SCALE]
[--decay DECAY] [--num_layer NUM_LAYER] [--emb_dim EMB_DIM]
[--dropout_ratio DROPOUT_RATIO]
[--tuning_type TUNING_TYPE]
[--dataset DATASET] [--model_file MODEL_FILE] [--seed SEED]
[--runseed RUNSEED]
[--num_layers NUM_LAYERS] [--pnum PNUM]
[--shot_number SHOT_NUMBER]
optional arguments:
--device Which gpu to use if any (default: 0)
--epochs Number of epochs to train (default: 100)
--lr Learning rate (default: 0.001)
--lr_scale Relative learning rate for the feature extraction layer (default: 1)
--decay Weight decay (default: 0)
--num_layer Number of GNN message passing layers (default: 5).
--emb_dim Embedding dimensions (default: 300)
--dropout_ratio Dropout ratio (default: 0.5)
--tuning_type 'gpf' for GPF and 'gpf-plus' for GPF-plus in the paper
--dataset Root directory of dataset. For now, only classification.
--model_file File path to read the model (if there is any)
--seed Seed for splitting the dataset.
--runseed Seed for minibatch selection, random initialization.
--split The way of dataset split(e.g., 'scaffold' for chem data)
--num_layers A range of [1,2,3]-layer MLPs with equal width
--pnum The number of independent basis for GPF-plus
--shot_number Number of shots
Parameter settings
We have provided scripts with hyper-parameter settings to reproduce the experimental results presented in our paper.
For the full-shot scenarios, you can obtain the experimental results by running run.sh.
sh run.sh
For the few-shot scenarios, you can obtain the experimental results by running run_few_shot.sh.
sh run_few_shot.sh
Citation
You can cite our paper by following bibtex.
@inproceedings{Fang2023UniversalPT,
title={Universal Prompt Tuning for Graph Neural Networks},
author={Taoran Fang and Yunchao Zhang and Yang Yang and Chunping Wang and Lei Chen},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}