This repository is a graph representation learning library, containing an implementation of Hyperbolic Graph Convolutions [1] in PyTorch, as well as multiple embedding approaches including:
- Shallow Euclidean
- Shallow Hyperbolic [2]
- Shallow Euclidean + Features (see [1])
- Shallow Hyperbolic + Features (see [1])
- Multi-Layer Perceptron (
MLP) - Hyperbolic Neural Networks (
HNN) [3]
- Graph Convolutional Neural Networks (
GCN) [4] - Graph Attention Networks (
GAT) [5] - Hyperbolic Graph Convolutions (
HGCN) [1]
All models can be trained for
- Link prediction (
lp) - Node classification (
nc)
If you don't have conda installed, please install it following the instructions here.
git clone https://github.com/HazyResearch/hgcn
cd hgcn
conda env create -f environment.yml
Alternatively, if you prefer to install dependencies with pip, please follow the instructions below:
virtualenv -p [PATH to python3.7 binary] hgcn
source hgcn/bin/activate
pip install -r requirements.txt
The data/ folder contains source files for:
- Cora
- Pubmed
- Disease
To run this code on new datasets, please add corresponding data processing and loading in load_data_nc and load_data_lp functions in utils/data_utils.py.
Before training, run
source set_env.sh
This will create environment variables that are used in the code.
This script trains models for link prediction and node classification tasks.
Metrics are printed at the end of training or can be saved in a directory by adding the command line argument --save=1.
optional arguments:
-h, --help show this help message and exit
--lr LR learning rate
--dropout DROPOUT dropout probability
--cuda CUDA which cuda device to use (-1 for cpu training)
--epochs EPOCHS maximum number of epochs to train for
--weight-decay WEIGHT_DECAY
l2 regularization strength
--optimizer OPTIMIZER
which optimizer to use, can be any of [Adam,
RiemannianAdam]
--momentum MOMENTUM momentum in optimizer
--patience PATIENCE patience for early stopping
--seed SEED seed for training
--log-freq LOG_FREQ how often to compute print train/val metrics (in
epochs)
--eval-freq EVAL_FREQ
how often to compute val metrics (in epochs)
--save SAVE 1 to save model and logs and 0 otherwise
--save-dir SAVE_DIR path to save training logs and model weights (defaults
to logs/task/date/run/)
--sweep-c SWEEP_C
--lr-reduce-freq LR_REDUCE_FREQ
reduce lr every lr-reduce-freq or None to keep lr
constant
--gamma GAMMA gamma for lr scheduler
--print-epoch PRINT_EPOCH
--grad-clip GRAD_CLIP
max norm for gradient clipping, or None for no
gradient clipping
--min-epochs MIN_EPOCHS
do not early stop before min-epochs
--task TASK which tasks to train on, can be any of [lp, nc]
--model MODEL which encoder to use, can be any of [Shallow, MLP,
HNN, GCN, GAT, HGCN]
--dim DIM embedding dimension
--manifold MANIFOLD which manifold to use, can be any of [Euclidean,
Hyperboloid, PoincareBall]
--c C hyperbolic radius, set to None for trainable curvature
--r R fermi-dirac decoder parameter for lp
--t T fermi-dirac decoder parameter for lp
--pretrained-embeddings PRETRAINED_EMBEDDINGS
path to pretrained embeddings (.npy file) for Shallow
node classification
--pos-weight POS_WEIGHT
whether to upweight positive class in node
classification tasks
--num-layers NUM_LAYERS
number of hidden layers in encoder
--bias BIAS whether to use bias (1) or not (0)
--act ACT which activation function to use (or None for no
activation)
--n-heads N_HEADS number of attention heads for graph attention
networks, must be a divisor dim
--alpha ALPHA alpha for leakyrelu in graph attention networks
--use-att USE_ATT whether to use hyperbolic attention in HGCN model
--double-precision DOUBLE_PRECISION
whether to use double precision
--dataset DATASET which dataset to use
--val-prop VAL_PROP proportion of validation edges for link prediction
--test-prop TEST_PROP
proportion of test edges for link prediction
--use-feats USE_FEATS
whether to use node features or not
--normalize-feats NORMALIZE_FEATS
whether to normalize input node features
--normalize-adj NORMALIZE_ADJ
whether to row-normalize the adjacency matrix
--split-seed SPLIT_SEED
seed for data splits (train/test/val)
We provide examples of training commands used to train HGCN and other graph embedding models for link prediction and node classification. In the examples below, we used a fixed random seed set to 1234 for reproducibility purposes. Note that results might slightly vary based on the machine used. To reproduce results in the paper, run each commad for 10 random seeds and average the results.
- Cora (Test ROC-AUC=93.79):
python train.py --task lp --dataset cora --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 None
- Pubmed (Test ROC-AUC: 95.17):
python train.py --task lp --dataset pubmed --model HGCN --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.4 --weight-decay 0.0001 --manifold PoincareBall --log-freq 5 --cuda 0
- Disease (Test ROC-AUC: 87.14):
python train.py --task lp --dataset disease_lp --model HGCN --lr 0.01 --dim 16 --num-layers 2 --num-layers 2 --act relu --bias 1 --dropout 0 --weight-decay 0 --manifold PoincareBall --normalize-feats 0 --log-freq 5
- Cora and Pubmed:
To train train a HGCN node classification model on Cora and Pubmed datasets, pre-train embeddings for link prediction as decribed in the previous section. Then train a MLP classifier using the pre-trained embeddings (embeddings.npy file saved in the save-dir directory). For instance for the Pubmed dataset:
python train.py --task nc --dataset pubmed --model Shallow --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.2 --weight-decay 0.0005 --manifold Euclidean --log-freq 5 --cuda 0 --use-feats 0 --pretrained-embeddings [PATH_TO_EMBEDDINGS]
- Disease (Test accuracy: 76.77):
python train.py --task nc --dataset disease_nc --model HGCN --dim 16 --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0 --weight-decay 0 --manifold PoincareBall --log-freq 5 --cuda 0
- Shallow Euclidean (Test ROC-AUC=86.40):
python train.py --task lp --dataset cora --model Shallow --manifold Euclidean --lr 0.01 --weight-decay 0.0005 --dim 16 --num-layers 0 --use-feats 0 --dropout 0.2 --act None --bias 0 --optimizer Adam --cuda 0
- Shallow Hyperbolic (Test ROC-AUC=85.97):
python train.py --task lp --dataset cora --model Shallow --manifold PoincareBall --lr 0.01 --weight-decay 0.0005 --dim 16 --num-layers 0 --use-feats 0 --dropout 0.2 --act None --bias 0 --optimizer RiemannianAdam --cuda 0
- GCN (Test ROC-AUC=89.22):
python train.py --task lp --dataset cora --model GCN --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.2 --weight-decay 0 --manifold Euclidean --log-freq 5 --cuda 0
- HNN (Test ROC-AUC=90.79):
python train.py --task lp --dataset cora --model HNN --lr 0.01 --dim 16 --num-layers 2 --act None --bias 1 --dropout 0.2 --weight-decay 0.001 --manifold PoincareBall --log-freq 5 --cuda 0 --c 1
- HNN (Test accuracy=68.20):
python train.py --task nc --dataset pubmed --model HNN --lr 0.01 --dim 16 --num-layers 2 --act None --bias 1 --dropout 0.5 --weight-decay 0 --manifold PoincareBall --log-freq 5 --cuda 0
- MLP (Test accuracy=73.00):
python train.py --task nc --dataset pubmed --model MLP --lr 0.01 --dim 16 --num-layers 2 --act None --bias 0 --dropout 0.2 --weight-decay 0.001 --manifold Euclidean --log-freq 5 --cuda 0
- GCN (Test accuracy=78.30):
python train.py --task nc --dataset pubmed --model GCN --lr 0.01 --dim 16 --num-layers 2 --act relu --bias 1 --dropout 0.7 --weight-decay 0.0005 --manifold Euclidean --log-freq 5 --cuda 0
- GAT (Test accuracy=78.50):
python train.py --task nc --dataset pubmed --model GAT --lr 0.01 --dim 16 --num-layers 2 --act elu --bias 1 --dropout 0.5 --weight-decay 0.0005 --alpha 0.2 --n-heads 4 --manifold Euclidean --log-freq 5 --cuda 0
- Hyperboloid implementation of HGCN
- Hyperbolic Attention in HGCN with local hyperbolic average
- More efficient negative sampling implementation for link prediction
[2] Nickel, M. and Kiela, D. Poincaré embeddings for learning hierarchical representations. NIPS 2017.
[3] Ganea, O., Bécigneul, G. and Hofmann, T. Hyperbolic neural networks. NIPS 2017.