GraphZoo

PyTorch version of GraphZoo.

Facilitating learning, using, and designing graph processing pipelines/models systematically.

We present a novel framework GraphZoo, that makes learning, using, and designing graph processing pipelines/models systematic by abstraction over the redundant components. The framework contains a powerful library that supports several hyperbolic manifolds and an easy-to-use modular framework to perform graph processing tasks which aids researchers in different components, namely, (i) reproduce evaluation pipelines of state-of-the-art approaches, (ii) design new hyperbolic or Euclidean graph networks and compare them against the state-of-the art approaches on standard benchmarks, (iii) add custom datasets for evaluation, (iv) add new tasks and evaluation criteria.

Installation

Using Github source:

git clone https://github.com/reddy-lab/GraphZoo.git
cd GraphZoo
python setup.py install

Using Pypi (under development, install from source):

pip install graphzoo

Getting Started in 60 Seconds

To train a Hyperbolic Graph Convolutional Networks model for node classification task on Cora dataset, make use of GraphZoo APIs customized loss functions and evaluation metrics for this task.

Prepare input data:

import graphzoo as gz
import torch
from graphzoo.config import parser

params = parser.parse_args(args=[])
params.dataset='cora'
params.datapath='data/cora'
data = gz.dataloader.DataLoader(params)

Initialize the model and fine-tune the hyperparameters:

params.task='nc'
params.model='HGCN'
params.manifold='PoincareBall'
params.dim=128
model= gz.models.NCModel(params)

Trainer is used to control the training flow:

optimizer = gz.optimizers.RiemannianAdam(params=model.parameters(), lr=params.lr, weight_decay=params.weight_decay)
trainer=gz.trainers.Trainer(params,model,optimizer,data)
trainer.run()
trainer.evaluate()

Getting Started Using Command Line

To train a Hyperbolic Graph Convolutional Networks model for node classification task on Cora dataset using command line:

cd GraphZoo
python graphzoo/trainers/train.py --task nc --dataset cora --datapath <your datapath> --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

Customizing Input Arguments

Various flags can be modified in the graphzoo.config module by the user.

DataLoader

     """
    GraphZoo Dataloader

    Input Parameters
    ----------
        'dataset': ('cora', 'which dataset to use, can be any of [cora, pubmed, airport, disease_nc, disease_lp] (type: str)')
        'datapath': (None, 'path to raw data (type: str)')
        'val-prop': (0.05, 'proportion of validation edges for link prediction (type:float)')
        'test-prop': (0.1, 'proportion of test edges for link prediction (type: float)')
        'use-feats': (1, 'whether to use node features (1) or not (0 in case of Shallow methods) (type: int)')
        'normalize-feats': (1, 'whether to normalize input node features (1) or not (0) (type: int)')
        'normalize-adj': (1, 'whether to row-normalize the adjacency matrix (1) or not(0) (type: int)')
        'split-seed': (1234, 'seed for data splits (train/test/val) (type: int)')

    API Input Parameters
    ----------
        args: list of above defined input parameters from `graphzoo.config`
    
    """

Models

    """
    Base model for graph embedding tasks

    Input Parameters
    ----------
        'task': ('nc', 'which tasks to train on, can be any of [lp, nc] (type: str)')
        'model': ('HGCN', 'which encoder to use, can be any of [Shallow, MLP, HNN, GCN, GAT, HGCN,HGAT] (type: str)')
        'dim': (128, 'embedding dimension (type: int)')
        'manifold': ('PoincareBall', 'which manifold to use, can be any of [Euclidean, Hyperboloid, PoincareBall] (type: str)')
        'c': (1.0, 'hyperbolic radius, set to None for trainable curvature (type: float)')
        'r': (2.0, 'fermi-dirac decoder parameter for lp (type: float)')
        't': (1.0, 'fermi-dirac decoder parameter for lp (type: float)')
        'pretrained-embeddings': (None, 'path to pretrained embeddings (.npy file) for Shallow node classification (type: str)')
        'num-layers': (2, 'number of hidden layers in encoder (type: int)')
        'bias': (1, 'whether to use bias (1) or not (0) (type: int)')
        'act': ('relu', 'which activation function to use or None for no activation (type: str)')
        'n-heads': (4, 'number of attention heads for graph attention networks, must be a divisor dim (type: int)')
        'alpha': (0.2, 'alpha for leakyrelu in graph attention networks (type: float)')
        'use-att': (0, 'whether to use hyperbolic attention (1) or not (0) (type: int)')
        'local-agg': (0, 'whether to local tangent space aggregation (1) or not (0) (type: int)')
        'n_classes': (7, 'number of classes in the dataset (type: int)')
        'n_nodes': (2708, 'number of nodes in the graph (type: int)')
        'feat_dim': (1433, 'feature dimension of the dataset (type: int)') 
        
    API Input Parameters
    ----------
        args: list of above defined input parameters from `graphzoo.config`
    """

Trainer

    """
    GraphZoo Trainer

    Input Parameters
    ----------
        'lr': (0.01, 'initial learning rate (type: float)')
        'dropout': (0.5, 'dropout probability (type: float)')
        'cuda': (-1, 'which cuda device to use or -1 for cpu training (type: int)')
        'device': ('cpu', 'which device to use cuda:$devicenumber for GPU or cpu for CPU (type: str)')
        'repeat': (10, 'number of times to repeat the experiment (type: int)')
        'optimizer': ('Adam', 'which optimizer to use, can be any of [Adam, RiemannianAdam, RiemannianSGD] (type: str)')
        'epochs': (5000, 'maximum number of epochs to train for (type:int)')
        'weight-decay': (0.001, 'l2 regularization strength (type: float)')
        'momentum': (0.999, 'momentum in optimizer (type: float)')
        'patience': (100, 'patience for early stopping (type: int)')
        'seed': (1234, 'seed for training (type: int)')
        'log-freq': (5, 'how often to compute print train/val metrics in epochs (type: int)')
        'eval-freq': (1, 'how often to compute val metrics in epochs (type: int)')
        'save': (0, '1 to save model and logs and 0 otherwise (type: int)')
        'save-dir': (None, 'path to save training logs and model weights (type: str)')
        'lr-reduce-freq': (None, 'reduce lr every lr-reduce-freq or None to keep lr constant (type: int)')
        'gamma': (0.5, 'gamma for lr scheduler (type: float)')
        'grad-clip': (None, 'max norm for gradient clipping, or None for no gradient clipping (type: float)')
        'min-epochs': (100, 'do not early stop before min-epochs (type: int)')
        'betas': ((0.9, 0.999), 'coefficients used for computing running averages of gradient and its square (type: Tuple[float, float])')
        'eps': (1e-8, 'term added to the denominator to improve numerical stability (type: float)')
        'amsgrad': (False, 'whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond` (type: bool)')
        'stabilize': (None, 'stabilize parameters if they are off-manifold due to numerical reasons every ``stabilize`` steps (type: int)')
        'dampening': (0,'dampening for momentum (type: float)')
        'nesterov': (False,'enables Nesterov momentum (type: bool)')

    API Input Parameters
    ----------
        args: list of above defined input parameters from `graphzoo.config`
        optimizer: a :class:`optim.Optimizer` instance
        model: a :class:`BaseModel` instance
    
    """

Customizing the Framework

Adding Custom Dataset

Add the dataset files in the data folder of the source code.
To run this code on new datasets, please add corresponding data processing and loading in load_data_nc and load_data_lp functions in dataloader/dataloader.py in the source code.

Output format for node classification dataloader is:

data = {'adj_train': adj, 'features': features, 'labels': labels, 'idx_train': idx_train, 'idx_val': idx_val, 'idx_test': idx_test}

Output format for link prediction dataloader is:

data = {'adj_train': adj_train, 'features': features, ‘train_edges’: train_edges, ‘train_edges_false’: train_edges_false,  ‘val_edges’: val_edges, ‘val_edges_false’: val_edges_false, ‘test_edges’: test_edges, ‘test_edges_false’: test_edges_false, 'adj_train_norm':adj_train_norm}

Adding Custom Layers

Attention layers can be added in layers/att_layers.py in the source code by adding a class in the file.
Hyperbolic layers can be added in layers/hyp_layers.py in the source code by adding a class in the file.
Other layers like a single GCN layer can be added in layers/layers.py in the source code by adding a class in the file.

Adding Custom Models

After adding custom layers, custom models can be added in models/encoders.py in the source code by adding a class in the file.
After adding custom layers, custom decoders to calculate the final output can be added in models/decoders.py in the source code by adding a class in the file. Default decoder is the LinearDecoder.

Datasets

The included datasets are the following and they need to be downloaded from the link:

Cora
Pubmed
Disease
Airport

Models

Shallow Methods

Shallow Euclidean
Shallow Hyperbolic

Neural Network Methods

Multi-Layer Perceptron (MLP)
Hyperbolic Neural Networks (HNN)

Graph Neural Network Methods

Graph Convolutional Neural Networks (GCN)
Graph Attention Networks (GAT)
Hyperbolic Graph Convolutions (HGCN)
Hyperbolic Graph Attention Networks (HGAT)

Package References

Tutorials (jupyter notebooks under development)

Documentation (under develpoment)

Code References

Some of the code was forked from the following repositories.

Model References

Citation

If you use GraphZooZoo in your research, please use the following BibTex entry.

@inproceedings{10.1145/3487553.3524241,
author = {Vyas, Anoushka and Choudhary, Nurendra and Khatir, Mehrdad and Reddy, Chandan K.},
title = {GraphZoo: A Development Toolkit for Graph Neural Networks with Hyperbolic Geometries},
year = {2022},
isbn = {9781450391306},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3487553.3524241},
doi = {10.1145/3487553.3524241},
booktitle = {Companion Proceedings of the Web Conference 2022},
keywords = {graph learning, graph neural network, hyperbolic models, software},
location = {Lyon, France},
series = {WWW '22}
}

License

GNU GPL v3