Heuristic Learning Graph Neural Network (HL-GNN)

This repository contains the official implementation of HL-GNN, as presented in the paper "Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction," accepted at KDD 2024.

Overview

HL-GNN is a novel method for link prediction that unifies local and global heuristics into a matrix formulation and implements it efficiently using graph neural networks. HL-GNN is simpler than GCN and can effectively reach up to 20 layers. It demonstrates effectiveness in link prediction tasks and scales well to large OGB datasets. Notably, HL-GNN requires only a few parameters for training (excluding the predictor) and is significantly faster than existing methods.

For more details, please refer to the paper.

Installation

Clone the repository and install the necessary dependencies:

git clone https://github.com/LARS-research/HL-GNN.git
cd HL-GNN
pip install -r requirements.txt

Usage

Planetoid Datasets

Cora

cd Planetoid
python planetoid.py --dataset cora --mlp_num_layers 3 --hidden_channels 8192 --dropout 0.5 --epochs 100 --K 20 --alpha 0.2 --init RWR

Citeseer

cd Planetoid
python planetoid.py --dataset citeseer --mlp_num_layers 2 --hidden_channels 8192 --dropout 0.5 --epochs 100 --K 20 --alpha 0.2 --init RWR

Pubmed

cd Planetoid
python planetoid.py --dataset pubmed --mlp_num_layers 3 --hidden_channels 512 --dropout 0.6 --epochs 300 --K 20 --alpha 0.2 --init KI

Amazon Datasets

Photo

cd Planetoid
python amazon.py --dataset photo --mlp_num_layers 3 --hidden_channels 512 --dropout 0.6 --epochs 200 --K 20 --alpha 0.2 --init RWR

Computers

cd Planetoid
python amazon.py --dataset computers --mlp_num_layers 3 --hidden_channels 512 --dropout 0.6 --epochs 200 --K 20 --alpha 0.2 --init RWR

OGB Datasets

ogbl-collab

cd OGB
python main.py --data_name ogbl-collab --predictor DOT --use_valedges_as_input True --year 2010 --epochs 800 --eval_last_best True --dropout 0.3 --use_node_feat True

ogbl-ddi

cd OGB
python main.py --data_name ogbl-ddi --emb_hidden_channels 512 --gnn_hidden_channels 512 --mlp_hidden_channels 512 --num_neg 3 --dropout 0.3 --loss_func WeightedHingeAUC

ogbl-ppa

cd OGB
python main.py --data_name ogbl-ppa --emb_hidden_channels 256 --mlp_hidden_channels 512 --gnn_hidden_channels 512 --grad_clip_norm 2.0 --epochs 500 --eval_steps 1 --num_neg 3 --dropout 0.5 --use_node_feat True --alpha 0.5 --loss_func WeightedHingeAUC

ogbl-citation2

cd OGB
python main.py --data_name ogbl-citation2 --emb_hidden_channels 64 --mlp_hidden_channels 256 --gnn_hidden_channels 256 --grad_clip_norm 1.0 --epochs 100 --eval_steps 1 --num_neg 3 --dropout 0.3 --eval_metric mrr --neg_sampler local --use_node_feat True --alpha 0.6

Results

The performance of HL-GNN on various datasets is summarized in the table below. The best and second-best performances are highlighted in bold and italic, respectively.

	Cora	Citeseer	Pubmed	Photo	Computers	collab	ddi	ppa	citation2
Method	Hits@100	Hits@100	Hits@100	AUC	AUC	Hits@50	Hits@20	Hits@100	MRR
SEAL	81.71	83.89	75.54	98.85	98.70	64.74	30.56	48.80	87.67
NBFNet	71.65	74.07	58.73	98.29	98.03	OOM	4.00	OOM	OOM
Neo-GNN	80.42	84.67	73.93	98.74	98.27	62.13	63.57	49.13	87.26
BUDDY	88.00	92.93	74.10	99.05	98.69	65.94	78.51	49.85	87.56
HL-GNN	94.22	94.31	88.15	99.11	98.82	68.11	80.27	56.77	89.43

Acknowledgement

We sincerely thank the PLNLP repository for providing an excellent pipeline that greatly facilitated our work on the OGB datasets.

Citation

If you find HL-GNN useful in your research, please cite our paper:

@inproceedings{zhang2024heuristic,
  title={Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction},
  author={Zhang, Juzheng and Wei, Lanning and Xu, Zhen and Yao, Quanming},
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2024}
}

Feel free to reach out if you have any questions!