Hyperbolic Graph Convolutional Auto-Encoders

Official PyTorch code of Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders

Jiwoong Park*, Junho Cho*, Hyung Jin Chang, Jin Young Choi (* indicates equal contribution)

Hyperbolic-1

Embeddings of cora dataset. GAE is Graph Auto-Encoders in Euclidean space, HGCAE is our method. P is Poincare ball, H is Hyperboloid.

Overview

This repository provides HGCAE code in PyTorch for reproducibility with

  • PoincareBall manifold
  • Link prediction task and node clustering task on graph data
    • 6 datasets: Cora, Citeseer, Wiki, Pubmed, Blog Catalog, Amazon Photo
    • Amazon Photo was downloaded via torch-geometric package.
  • Image clustering task on images
    • 2 datasets: ImageNet10, ImageNetDog
    • Image features extracted from ImageNet10, ImageNetDog with PICA image clustering algorithm
    • Mutual K-NN graph from the image features provided.
  • ImageNet-BNCR
    • We have constructed a new dataset, ImageNet-BNCR(Balanced Number of Classes across Roots), via randomly choosing 3 leaf classes per root. We chose three roots, Artifacts, Natural objects, and Animal. Thus, there exist 9 leaf classes, and each leaf class contains 1,300 images in ImageNet-BNCR dataset.

tree_BNCR

Installation Guide

We use docker to reproduce performance. Please refer guide.md

Usage

1. Run docker

Before training, run our docker image:

docker run --gpus all -it --rm --shm-size 100G -v $PWD:/workspace junhocho/hyperbolicgraphnn:8 bash

If you want to cache edge splits for train/val dataset and load faster afterwards, mkdir ~/tmp and run:

docker run --gpus all -it --rm --shm-size 100G -v $PWD:/workspace -v ~/tmp:/root/tmp junhocho/hyperbolicgraphnn:8 bash

2. train_<dataset>.sh

In the docker session, run each train shell script for each dataset to reproduce performance:

Graph data link prediction

Run following commands to reproduce results:

  • sh script/train_cora_lp.sh
  • sh script/train_citeseer_lp.sh
  • sh script/train_wiki_lp.sh
  • sh script/train_pubmed_lp.sh
  • sh script/train_blogcatalog_lp.sh
  • sh script/train_amazonphoto_lp.sh
ROC AP
Cora 0.94890703 0.94726805
Citeseer 0.96059407 0.96305937
Wiki 0.95510805 0.96200790
Pubmed 0.96207212 0.96083080
Blog Catalog 0.89683939 0.88651569
Amazon Photo 0.98240673 0.97655753

Graph data node clustering

  • sh script/train_cora_nc.sh
  • sh script/train_citeseer_nc.sh
  • sh script/train_wiki_nc.sh
  • sh script/train_pubmed_nc.sh
  • sh script/train_blogcatalog_nc.sh
  • sh script/train_amazonphoto_nc.sh
ACC NMI ARI
Cora 0.74667651 0.57252940 0.55212928
Citeseer 0.69311692 0.42249294 0.44101404
Wiki 0.45945946 0.46777881 0.21517031
Pubmed 0.74849115 0.37759262 0.40770875
Blog Catalog 0.55061586 0.32557388 0.25227964
Amazon Photo 0.78130719 0.69623651 0.60342107

Image clustering

  • sh script/train_ImageNet10.sh
  • sh script/train_ImageNetDog.sh
ACC NMI ARI
ImageNet10 0.85592308 0.79019131 0.74181220
ImageNetDog 0.38738462 0.36059650 0.22696503
  • At least 11GB VRAM is required to run on Pubmed, BlogCatalog, Amazon Photo.
  • We have used GTX 1080ti only in our experiments.
  • Other gpu architectures may not reproduce above performance.

Parameter description

  • dataset : Choose dataset. Refer to each training scripts.
  • c : Curvature of hypebolic space. Should be >0. Preferably choose from 0.1, 0.5 ,1 ,2.
  • c_trainable : 0 or 1. Train c if 1.
  • dropout : Dropout ratio.
  • weight_decay : Weight decay.
  • hidden_dim : Hidden layer dimension. Same dimension used in encoder and decoder.
  • dim : Embedding dimension.
  • lambda_rec : Input reconstruction loss weight.
  • act : relu, elu, tanh.
  • --manifold PoincareBall : Use Euclidean if training euclidean models.
  • --node-cluster 1 : If specified perform node clustering task. If not, link prediction task.

Acknowledgments

This repo is inspired by hgcn.

And some of the code was forked from the following repositories:

License

This work is licensed under the MIT License

Citation

@inproceedings{park2021unsupervised,
  title={Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders},
  author={Jiwoong Park and Junho Cho and Hyung Jin Chang and Jin Young Choi},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2021}
}

GitHub

https://github.com/junhocho/HGCAE