subgraph-sketching (Work in Progress)

Running ogbl-vessel

I created this fork in an attempt to run BUDDY on the ogbl-vessel dataset. I used the following command to run BUDDY on vessel.

python runners/run.py --dataset_name ogbl-vessel --model BUDDY --batch_size 256 --eval_batch_size 256 --feature_dropout 0.05 --label_dropout 0.1 --reps 10 --epochs 50 --num_workers 48

The only code changes made are to support this additional dataset.

Introduction

This is a reimplementation of the code used for "Graph Neural Networks for Link Prediction with Subgraph Sketching" https://openreview.net/pdf?id=m1oqEOAozQU which was accepted for oral presentation (top 5% of accepted papers) at ICLR 2023.

The high level structure of the code will not change, but some details such as default parameter setting remain work in progress

Running experiments

Requirements

Dependencies (with python >= 3.9): Main dependencies are

pytorch==1.13

torch_geometric==2.2.0

torch-scatter==2.1.1+pt113cpu

torch-sparse==0.6.17+pt113cpu

torch-spline-conv==1.2.2+pt113cpu

Example commands to install the dependencies in a new conda environment (tested on Linux machine without GPU).

conda create --name ss python=3.9
conda activate ss
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 -c pytorch
pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cpu.html
pip install torch_geometric
pip install fast-pagerank wandb datasketch ogb

For GPU installation:

conda create --name ss python=3.9
conda activate ss
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu116.html
pip install torch_geometric
pip install fast-pagerank wandb datasketch ogb

if you are unfamiliar with wandb, quickstart instructions are pip install wandb

Experiments

To run experiments

cd src
python runners/run.py --dataset Cora --model ELPH
python runners/run.py --dataset Cora --model BUDDY
python runners/run.py --dataset Citeseer --model ELPH
python runners/run.py --dataset Citeseer --model BUDDY
python runners/run.py --dataset Pubmed --max_hash_hops 3 --feature_dropout 0.2 --model ELPH
python runners/run.py --dataset Pubmed --max_hash_hops 3 --feature_dropout 0.2 --model BUDDY
python runners/run.py --dataset ogbl-collab --feature_dropout 0.05 --label_dropout 0.1 --year 2007 --model ELPH
python runners/run.py --dataset ogbl-collab --feature_dropout 0.05 --label_dropout 0.1 --year 2007 --model BUDDY
python runners/run.py --dataset ogbl-ppa --label_dropout 0.1 --use_RA --model ELPH
python runners/run.py --dataset ogbl-ppa --label_dropout 0.1 --use_RA --model BUDDY
python runners/run.py --dataset ogbl-ddi --train_node_embedding --propagate_embeddings --epochs 120 --num_negs 6 --model ELPH
python runners/run.py --dataset ogbl-ddi --train_node_embedding --propagate_embeddings --epochs 120 --num_negs 6 --model BUDDY
python runners/run.py --dataset ogbl-citation2 --hidden_channels 128 --num_negs 5 --sign_dropout 0.2 --sign_k 3 --model ELPH
python runners/run.py --dataset ogbl-citation2 --hidden_channels 128 --num_negs 5 --sign_dropout 0.2 --sign_k 3 --model BUDDY

You may need to adjust

--batch_size

and

--eval_batch_size

based on available (GPU) memory Most of the runtime of BUDDY is building hashes and subgraph features. If you intend to run BUDDY more than once, then set the flag

--cache_subgraph_features

to store subgraph features on disk and read them if previously cached.

Dataset and Preprocessing

Create a root level folder

./dataset

Cite us

If you found this work useful, please cite our paper

@inproceedings
{chamberlain2023graph,
  title={Graph Neural Networks for Link Prediction with Subgraph Sketching},
  author={Chamberlain, Benjamin Paul and Shirobokov, Sergey and Rossi, Emanuele and Frasca, Fabrizio and Markovich, Thomas and Hammerla, Nils and     Bronstein, Michael M and Hansmire, Max},
  booktitle={ICLR}
  year={2023}
}