ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

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

Environment

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

and their dependencies.

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.
  • An example, for link prediction (LP) task on Cora dataset: python 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

Thanks

Some of the code was forked from the following repositories:

• HazyResearch/HGCN;
• SunQingYun1996/SUGAR;
• tocom242242/nash_q_learning.

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