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Code for Message Passing Link Predictor (MPLP)

Neurips 2024 Paper: Pure Message Passing Can Estimate Common Neighbor for Link Prediction

Authors: Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla

Framework of MPLP

Introduction

This repository contains the code for the paper Pure Message Passing Can Estimate Common Neighbor for Link Prediction. MPLP is a simple yet effective message passing framework for link prediction. It is based on the observation that the common neighbor count can be estimated by performing message passing on the random vectors. MPLP is able to achieve state-of-the-art performance on a wide range of datasets on various domains, including social networks, biological networks, and citation networks. Moreover, MPLP is able to achieve new state-of-the-art performance on the OGB leaderboard as of 2024-10-08:

PPA Citation2
Metric Hits@100 MRR
Current SOTA 63.22±1.74 90.18±0.15
MPLP+ 65.24±1.50 90.72±0.12

Environment Setting

conda create -n mplp
conda install -n mplp pytorch torchvision torchaudio pytorch-cuda=11.8 pyg=2.3.0 pytorch-sparse=0.6.17 -c pytorch -c nvidia -c pyg
conda activate mplp
pip install ogb torch-hd

Data Preparation

Part of the data has been included in the repository at ./data/. For the rest of the data, it will be automatically downloaded by the code.

Usage

To run experiments:

python main.py --dataset=physics --batch_size=2048 --use_degree=mlp --minimum_degree_onehot=60 --mask_target=True

In MPLP, there are couple of hyperparameters that can be tuned, including:

  • --dataset: the name of the dataset to be used.
  • --predictor: the predictor to be used. It can be MPLP or MPLP+.
  • --batch_size: the batch size.
  • --signature_dim: the node signature dimension F in MPLP.
  • --mask_target: whether to mask the target node in the training set to remove the shortcut.
  • --use_degree: the methods to rescale the norm of random vectors.
  • --minimum_degree_onehot: the minimum degree of hubs with onehot encoding to reduce variance.

Experiment Reproduction

Commands to reproduce the results of MPLP reported in the paper

USAir

python main.py --dataset=USAir --xdp=0.8 --feat_dropout=0.05 --label_dropout=0.2 --use_embedding=True --batch_size=512 --weight_decay=0.001 --lr=0.0015 --encoder=puregcn

NS

python main.py --dataset=NS --xdp=0.5 --feat_dropout=0.05 --label_dropout=0.2 --batch_size=512 --use_degree=mlp --lr=0.01

PB

python main.py --dataset=PB --xdp=0.8 --feat_dropout=0.2 --label_dropout=0.6 --batch_size=512 --lr=0.0015 --batchnorm_affine=False

Yeast

python main.py --dataset=Yeast --xdp=0.8 --feat_dropout=0.2 --label_dropout=0.2 --use_embedding=True --batch_size=512 --use_degree=RA --lr=0.0015 --encoder=puregcn --batchnorm_affine=False

C.ele

python main.py --dataset=Celegans --xdp=0.8 --feat_dropout=0.6 --use_embedding=True --batch_size=512 --weight_decay=0.001 --use_degree=AA --lr=0.0015

Power

python main.py --dataset=Power --xdp=0.8 --feat_dropout=0.2 --label_dropout=0.05 --use_embedding=True --batch_size=512 --weight_decay=0.001 --lr=0.0015 --encoder=puregcn

Router

python main.py --dataset=Router --xdp=0.8 --feat_dropout=0.2 --label_dropout=0.05 --batch_size=512 --use_degree=mlp --encoder=puregcn

E.coli

python main.py --dataset=Ecoli --xdp=0.8 --feat_dropout=0.05 --label_dropout=0.6 --use_embedding=True --batch_size=512 --use_degree=RA --batchnorm_affine=False

CS

python main.py --dataset=cs --xdp=0.5 --feat_dropout=0.6 --label_dropout=0.05 --batch_size=4096 --use_degree=mlp --lr=0.01 --batchnorm_affine=False --patience=40

Physics

python main.py --dataset=physics --xdp=0.1 --feat_dropout=0.2 --label_dropout=0.05 --batch_size=4096 --use_degree=mlp --encoder=puregcn --batchnorm_affine=False --patience=40

Computers

python main.py --dataset=computers --xdp=0.1 --feat_dropout=0.2 --label_dropout=0.2 --batch_size=4096 --use_degree=mlp --minimum_degree_onehot=80 --use_embedding=True --patience=40

Photo

python main.py --dataset=photos --xdp=0.5 --feat_dropout=0.05 --label_dropout=0.6 --batch_size=4096 --use_degree=mlp --minimum_degree_onehot=80 --lr=0.01 --use_embedding=True --batchnorm_affine=False --patience=40

Collab

python main.py --dataset=ogbl-collab --use_embedding=True --batch_size=32768 --use_degree=mlp --patience=40 --log_steps=1 --year=2010 --use_valedges_as_input=True --xdp=0.8 --feat_dropout=0.6 --label_dropout=0.2

Collab (no feat)

python main.py --dataset=ogbl-collab --use_feature=False --batch_size=8192 --mask_target=True --weight_decay=0 --use_degree=mlp --patience=40 --log_steps=1 --minimum_degree_onehot=50 --year=2010 --use_valedges_as_input=True --signature_dim=6000

Vessel

python main.py --dataset=ogbl-vessel --batch_size=32768 --test_batch_size=32768 --predictor=MPLP --use_degree=RA --patience=20 --log_steps=1 --xdp=0.5 --feat_dropout=0.6 --label_dropout=0.05 --metric=AUC
Commands to reproduce the results of MPLP+ reported in the paper

PPA

python main.py --dataset=ogbl-ppa --use_feature=False --batch_size=32768 --predictor=MPLP+ --use_degree=RA --patience=20 --log_steps=1 --xdp=0.5 --label_dropout=0.6 --use_embedding=True --metric=Hits@100 --runs=10 --test_batch_size=32768

Citation2

python main.py --dataset=ogbl-citation2 --use_feature=False --batch_size=261424 --predictor=MPLP+ --use_degree=RA --patience=20 --log_steps=1 --xdp=0.8 --feat_dropout=0.05 --label_dropout=0.6 --encoder=puregcn --use_embedding=True --signature_dim=512 --minimum_degree_onehot=1500 --test_batch_size=3000000

USAir

python main.py --dataset=USAir --xdp=0.8 --feat_dropout=0.05 --label_dropout=0.6 --use_embedding=True --batch_size=512 --weight_decay=0.001 --use_degree=RA --lr=0.0015 --encoder=puregcn --batchnorm_affine=False --predictor=MPLP+

NS

python main.py --dataset=NS --xdp=0.5 --feat_dropout=0.05 --label_dropout=0.2 --batch_size=512 --weight_decay=0.001 --use_degree=mlp --encoder=puregcn --batchnorm_affine=False --predictor=MPLP+

PB

python main.py --dataset=PB --xdp=0.8 --feat_dropout=0.2 --use_embedding=True --batch_size=512 --batchnorm_affine=False --predictor=MPLP+

Yeast

python main.py --dataset=Yeast --xdp=0.8 --feat_dropout=0.05 --label_dropout=0.05 --use_embedding=True --batch_size=512 --use_degree=RA --encoder=puregcn --predictor=MPLP+

C.ele

python main.py --dataset=Celegans --xdp=0.5 --feat_dropout=0.6 --use_embedding=True --batch_size=512 --use_degree=RA --lr=0.0015 --predictor=MPLP+

Power

python main.py --dataset=Power --xdp=0.8 --label_dropout=0.2 --use_embedding=True --batch_size=512 --weight_decay=0.001 --lr=0.0015 --encoder=puregcn --predictor=MPLP+

Router

python main.py --dataset=Router --xdp=0.8 --feat_dropout=0.05 --label_dropout=0.05 --batch_size=512 --use_degree=mlp --predictor=MPLP+

E.coli

python main.py --dataset=Ecoli --xdp=0.8 --feat_dropout=0.05 --label_dropout=0.6 --use_embedding=True --batch_size=512 --batchnorm_affine=False --predictor=MPLP+

CS

python main.py --dataset=cs --xdp=0.5 --feat_dropout=0.2 --label_dropout=0.2 --batch_size=4096 --predictor=MPLP+ --use_degree=mlp --minimum_degree_onehot=150 --encoder=puregcn --patience=40

Physics

python main.py --dataset=physics --xdp=0.5 --feat_dropout=0.2 --label_dropout=0.2 --batch_size=4096 --predictor=MPLP+ --use_degree=mlp --encoder=puregcn --batchnorm_affine=False --patience=40

Computers

python main.py --dataset=computers --xdp=0.8 --label_dropout=0.05 --batch_size=4096 --predictor=MPLP+ --use_degree=AA --minimum_degree_onehot=150 --use_embedding=True --patience=40

Photo

python main.py --dataset=photos --xdp=0.8 --label_dropout=0.2 --batch_size=4096 --predictor=MPLP+ --use_degree=mlp --minimum_degree_onehot=150 --encoder=puregcn --use_embedding=True --batchnorm_affine=False --patience=40

Collab

python main.py --dataset=ogbl-collab --use_embedding=True --batch_size=32768 --predictor=MPLP+ --use_degree=mlp --patience=40 --log_steps=1 --minimum_degree_onehot=100 --year=2010 --use_valedges_as_input=True --xdp=0.8 --feat_dropout=0.2 --label_dropout=0.2

Vessel

python main.py --dataset=ogbl-vessel --use_feature=False --batch_size=32768 --test_batch_size=32768 --predictor=MPLP+ --patience=20 --log_steps=1 --xdp=0.5 --feat_dropout=0.2 --label_dropout=0.05 --metric=AUC

Citation

If you find this repository useful in your research, please cite the following paper:

@article{dong2023pure,
  title={Pure Message Passing Can Estimate Common Neighbor for Link Prediction},
  author={Dong, Kaiwen and Guo, Zhichun and Chawla, Nitesh V},
  journal={arXiv preprint arXiv:2309.00976},
  year={2023}
}