Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

Last update: Nov 28, 2022

Related tags

Deep Learning ACE-HGNN

Overview

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:

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

Comments

Dataset evaluation results

Hello, I'm reading your paper. When I saw the experimental chapter, I found a very strange result, that is, on the Citeseer data set, the node classification score is generally less than 75. How do you measure such high data? Is the division based on the public 20 labels of each type of node? Is it the same dataset as the original GCN or GAT?

opened by DuanhaoranCC 0

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Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

Related tags

Overview

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

Environment

Usage

1. Setup

2. Usage

Thanks

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Comments

Dataset evaluation results

Owner

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