ACE-HGNN in Pytorch

This repository is the implementation of ACE-HGNN in PyTorch.

[ICDM 2021] ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

Environment

Below are some essential pypi packages we use on our own experiment environment. You may install their dependencies at the same time.

python==3.6.8
pytorch==1.6.0
nashpy==0.0.21
networkx==2.2
scikit-learn==0.20.3
numpy==1.16.2
pandas==0.24.2
scipy==1.2.1

Usage

1. Setup

Clone this repo

Create a virtual environment using conda or virtualenv.

conda env create -f environment.yml
virtualenv -p [PATH to python3.6 binary] ace-hgnn

Enter the virtual environment and run pip install -r requirements.txt.

2. Usage

Run set_env.sh in the command line. (Linux)
Please refer to config.py for our Model's full parameters and their default values.
Run python train.py [--param param_value] to train our model, with setting custom parameters.
Some training examples:

Dataset	Task	Command	Test AUC/F1
Cora	LP	python -u train.py --task lp --dataset cora --model HGCN --lr 0.005 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.5 --weight-decay 0.001 --manifold PoincareBall --log-freq 5 --cuda 0 --c 1.0 --epochs 2000 --lr-reduce-freq 200 --lr-q 0.5	93.94
Cora	NC	python -u train.py --task nc --dataset cora --model HGCN --lr 0.01 --dim 16 --num-layers 2 --act leaky_relu --bias 1 --dropout 0.1 --weight-decay 0.0005 --manifold PoincareBall --log-freq 5 --cuda 0 --c 1.0 --epochs 2000 --lr-reduce-freq 200 --lr-q 0.5	81.80
PubMed	LP	python -u train.py --task lp --dataset pubmed --model HGCN --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.5 --weight-decay 0.001 --manifold PoincareBall --log-freq 5 --cuda 0 --c 1.0 --lr-q 0.5 --save 1 --theta 0.5 --lr-reduce-freq None --gamma 0.2 --seed 4789	95.11
WebKB	LP	python -u train.py --task lp --dataset webkb --model HGCN --lr 0.005 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.5 --weight-decay 0.001 --manifold PoincareBall --log-freq 5 --cuda 0 --c 1.0 --epochs 2000 --lr-reduce-freq 200 --lr-q 0.5	94.47
PPI	LP	python -u train.py --task lp --dataset ppi --model HGCN --lr 0.0005 --dim 16 --num-layers 2 --act leaky_relu --bias 1 --dropout 0.05 --weight-decay 0.0005 --manifold PoincareBall --log-freq 5 --c 1.0 --epochs 5000 --lr-reduce-freq 500 --lr-q 0.5	92.03
PPI	NC	python -u train.py --task nc --dataset ppi --model HGCN --dim 16 --num-layers 2 --bias 1 --dropout 0.05 --weight-decay 0.0005 --manifold PoincareBall --log-freq 5 --c 1.0 --epochs 1000 --lr-reduce-freq 100 --lr-q 0.5 --lr 0.0005 --act tanh	67.54

Thanks

Some of the code was forked from the following repositories:

We deeply thanks for their contributions to the open-source community.

We also thanks for the computing infrastructure provided by Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC).

Citation

@inproceedings{fu2021ace,
  title={ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network},
  author={Fu, Xingcheng and Li, Jianxin and Wu, Jia and Sun, Qingyun and Ji, Cheng and Wang, Senzhang and Tan, Jiajun and Peng, Hao and Philip, S Yu},
  booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
  pages={111--120},
  year={2021},
  organization={IEEE Computer Society}
}