ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning

Introduction

This repo contains the Pytorch [1] implementation of Adversarial Graph Contrastive Learning (AD-GCL) principle instantiated with learnable edge dropping augmentation. The paper is available on arxiv.

Requirements and Environment Setup

Code developed and tested in Python 3.8.8 using PyTorch 1.8. Please refer to their official websites for installation and setup.

Some major requirements are given below

numpy~=1.20.1
networkx~=2.5.1
torch~=1.8.1
tqdm~=4.60.0
scikit-learn~=0.24.1
pandas~=1.2.4
gensim~=4.0.1
scipy~=1.6.2
ogb~=1.3.1
matplotlib~=3.4.2
torch-cluster~=1.5.9
torch-geometric~=1.7.0
torch-scatter~=2.0.6
torch-sparse~=0.6.9
torch-spline-conv~=1.2.1
rdkit~=2021.03.1

Datasets

The package datasets contains the modules required for downloading and loading the TU Benchmark Dataset, ZINC and transfer learning pre-train and fine-tuning datasets.

Create a folder to store all datasets using mkdir original_datasets. Except for the transfer learning datasets all the others are automatically downloaded and loaded using the datasets package. Follow and download chem and bio datasets for transfer learning from here and place it inside a newly created folder called transfer within original_datasets.

The Open Graph Benchmark datasets are downloaded and loaded using the ogb library. Please refer here for more details and installation.

AD-GCL Training

For running AD-GCL on Open Graph Benchmark. e.g. CUDA_VISIBLE_DEVICES=0 python test_minmax_ogbg.py --dataset ogbg-molesol --reg_lambda 0.4

usage: test_minmax_ogbg.py [-h] [--dataset DATASET] [--model_lr MODEL_LR] [--view_lr VIEW_LR] [--num_gc_layers NUM_GC_LAYERS] [--pooling_type POOLING_TYPE] [--emb_dim EMB_DIM] [--mlp_edge_model_dim MLP_EDGE_MODEL_DIM] [--batch_size BATCH_SIZE] [--drop_ratio DROP_RATIO]
                           [--epochs EPOCHS] [--reg_lambda REG_LAMBDA] [--seed SEED]

AD-GCL ogbg-mol*

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     Dataset
  --model_lr MODEL_LR   Model Learning rate.
  --view_lr VIEW_LR     View Learning rate.
  --num_gc_layers NUM_GC_LAYERS
                        Number of GNN layers before pooling
  --pooling_type POOLING_TYPE
                        GNN Pooling Type Standard/Layerwise
  --emb_dim EMB_DIM     embedding dimension
  --mlp_edge_model_dim MLP_EDGE_MODEL_DIM
                        embedding dimension
  --batch_size BATCH_SIZE
                        batch size
  --drop_ratio DROP_RATIO
                        Dropout Ratio / Probability
  --epochs EPOCHS       Train Epochs
  --reg_lambda REG_LAMBDA
                        View Learner Edge Perturb Regularization Strength
  --seed SEED

Similarly, one can run for ZINC and TU datasets using for e.g. CUDA_VISIBLE_DEVICES=0 python test_minmax_zinc.py and CUDA_VISIBLE_DEVICES=0 python test_minmax_tu.py --dataset REDDIT-BINARY respectively. Adding a --help at the end will provide more details.

Pretraining for transfer learning

usage: test_minmax_transfer_pretrain_chem.py [-h] [--dataset DATASET] [--model_lr MODEL_LR] [--view_lr VIEW_LR] [--num_gc_layers NUM_GC_LAYERS] [--pooling_type POOLING_TYPE] [--emb_dim EMB_DIM] [--mlp_edge_model_dim MLP_EDGE_MODEL_DIM] [--batch_size BATCH_SIZE]
                                             [--drop_ratio DROP_RATIO] [--epochs EPOCHS] [--reg_lambda REG_LAMBDA] [--seed SEED]

Transfer Learning AD-GCL Pretrain on ZINC 2M

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     Dataset
  --model_lr MODEL_LR   Model Learning rate.
  --view_lr VIEW_LR     View Learning rate.
  --num_gc_layers NUM_GC_LAYERS
                        Number of GNN layers before pooling
  --pooling_type POOLING_TYPE
                        GNN Pooling Type Standard/Layerwise
  --emb_dim EMB_DIM     embedding dimension
  --mlp_edge_model_dim MLP_EDGE_MODEL_DIM
                        embedding dimension
  --batch_size BATCH_SIZE
                        batch size
  --drop_ratio DROP_RATIO
                        Dropout Ratio / Probability
  --epochs EPOCHS       Train Epochs
  --reg_lambda REG_LAMBDA
                        View Learner Edge Perturb Regularization Strength
  --seed SEED

usage: test_minmax_transfer_pretrain_bio.py [-h] [--dataset DATASET] [--model_lr MODEL_LR] [--view_lr VIEW_LR] [--num_gc_layers NUM_GC_LAYERS] [--pooling_type POOLING_TYPE] [--emb_dim EMB_DIM] [--mlp_edge_model_dim MLP_EDGE_MODEL_DIM] [--batch_size BATCH_SIZE]
                                            [--drop_ratio DROP_RATIO] [--epochs EPOCHS] [--reg_lambda REG_LAMBDA] [--seed SEED]

Transfer Learning AD-GCL Pretrain on PPI-306K

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     Dataset
  --model_lr MODEL_LR   Model Learning rate.
  --view_lr VIEW_LR     View Learning rate.
  --num_gc_layers NUM_GC_LAYERS
                        Number of GNN layers before pooling
  --pooling_type POOLING_TYPE
                        GNN Pooling Type Standard/Layerwise
  --emb_dim EMB_DIM     embedding dimension
  --mlp_edge_model_dim MLP_EDGE_MODEL_DIM
                        embedding dimension
  --batch_size BATCH_SIZE
                        batch size
  --drop_ratio DROP_RATIO
                        Dropout Ratio / Probability
  --epochs EPOCHS       Train Epochs
  --reg_lambda REG_LAMBDA
                        View Learner Edge Perturb Regularization Strength
  --seed SEED

Pre-train models will be automatically saved in a folder called models_minmax. Please use those when finetuning to initialize the GNN. More details below.

Fine-tuning for evaluating transfer learning

For fine-tuning evaluation for transfer learning.

usage: test_transfer_finetune_chem.py [-h] [--device DEVICE] [--batch_size BATCH_SIZE] [--epochs EPOCHS] [--lr LR] [--lr_scale LR_SCALE] [--decay DECAY] [--num_layer NUM_LAYER] [--emb_dim EMB_DIM] [--dropout_ratio DROPOUT_RATIO] [--graph_pooling GRAPH_POOLING] [--JK JK]
                                      [--gnn_type GNN_TYPE] [--dataset DATASET] [--input_model_file INPUT_MODEL_FILE] [--seed SEED] [--split SPLIT] [--eval_train EVAL_TRAIN] [--num_workers NUM_WORKERS]

Finetuning Chem after pre-training of graph neural networks

optional arguments:
  -h, --help            show this help message and exit
  --device DEVICE       which gpu to use if any (default: 0)
  --batch_size BATCH_SIZE
                        input batch size for training (default: 32)
  --epochs EPOCHS       number of epochs to train (default: 100)
  --lr LR               learning rate (default: 0.001)
  --lr_scale LR_SCALE   relative learning rate for the feature extraction layer (default: 1)
  --decay DECAY         weight decay (default: 0)
  --num_layer NUM_LAYER
                        number of GNN message passing layers (default: 5).
  --emb_dim EMB_DIM     embedding dimensions (default: 300)
  --dropout_ratio DROPOUT_RATIO
                        dropout ratio (default: 0.5)
  --graph_pooling GRAPH_POOLING
                        graph level pooling (sum, mean, max, set2set, attention)
  --JK JK               how the node features across layers are combined. last, sum, max or concat
  --gnn_type GNN_TYPE
  --dataset DATASET     dataset. For now, only classification.
  --input_model_file INPUT_MODEL_FILE
                        filename to read the pretrain model (if there is any)
  --seed SEED           Seed for minibatch selection, random initialization.
  --split SPLIT         random or scaffold or random_scaffold
  --eval_train EVAL_TRAIN
                        evaluating training or not
  --num_workers NUM_WORKERS
                        number of workers for dataset loading

Similarly, for the bio dataset use python test_transfer_finetune_bio.py --help for details.

Please refer to the appendix of our paper for more details regarding hyperparameter settings.

Acknowledgements

This reference implementation is inspired and based on earlier works [2] and [3].

Please cite our paper if you use this code in your own work.

@article{suresh2021adversarial,
  title={Adversarial Graph Augmentation to Improve Graph Contrastive Learning},
  author={Suresh, Susheel and Li, Pan and Hao, Cong and Neville, Jennifer},
  journal={arXiv preprint arXiv:2106.05819},
  year={2021}
}

References

[1] Paszke, Adam, et al. "PyTorch: An Imperative Style, High-Performance Deep Learning Library." Advances in Neural Information Processing Systems 32 (2019): 8026-8037.

[2] Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations”. Advances in Neural Information Processing Systems, vol. 33, 2020

[3] Weihua Hu*, Bowen Liu*, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec. "Strategies for Pre-training Graph Neural Networks". ICLR 2020

Adversarial Graph Augmentation to Improve Graph Contrastive Learning

Related tags

Overview

ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning

Introduction

Requirements and Environment Setup

Datasets

AD-GCL Training

Pretraining for transfer learning

Fine-tuning for evaluating transfer learning

Acknowledgements

References

Owner

susheel suresh

Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

Measure WWjj polarization fraction

PanopticBEV - Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images

Style transfer, deep learning, feature transform

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches

optimization routines for hyperparameter tuning

This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

Data Preparation, Processing, and Visualization for MoVi Data

📚 A collection of Jupyter notebooks for learning and experimenting with OpenVINO 👓

SwinIR: Image Restoration Using Swin Transformer

Greedy Gaussian Segmentation

Reproducing code of hair style replacement method from Barbershorp.

Weakly-supervised object detection.

Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT)

A pre-trained language model for social media text in Spanish

Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation"

ISNAS-DIP: Image Specific Neural Architecture Search for Deep Image Prior [CVPR 2022]