Awesome

Hyperbolic Graph Convolutional Auto-Encoders

Accepted to CVPR2021 :tada:

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)}

_{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.
  • <img width="600" alt="bncr" src="./images/tree_BNCR.png">

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

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

Image clustering

• sh script/train_ImageNet10.sh
• sh script/train_ImageNetDog.sh

• 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:

• pygcn
• gae
• hyperbolic-image-embeddings
• pyGAT
• poincare-embeddings
• geoopt

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}
}