/GSLB

A comprehensive benchmark of Graph Structure Learning

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Graph Structure Learning Benchmark (GSLB)

GSLB is a Graph Structure Learning (GSL) library and benchmark based on DGL and PyTorch. We integrate diverse datasets and state-of-the-art GSL models.

📔 What is Graph Structure Learning?

pipeline

Graph Structure Learning (GSL) aims to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. GSL methods start with input features and an optimal initial graph structure. Its corresponding computation graph is iteratively refined through a structure learning module. With the refined computation graph ,GNNs are used to generate graph representations. Then parameters of the GNNs and the structure modeling module are jointly updated, either simultaneously or alternatively, util a preset stopping condition is satisfied.

If you want to explore more information about GSL, please refer to our survey and paper collection.

🚀 Get Start

Requirements

GSLB needs the following requirements to be satisfied beforehand:

  • Python 3.8+
  • PyTorch 1.13
  • DGL 1.1+
  • Scipy 1.9+
  • Scikit-learn
  • Numpy
  • NetworkX
  • ogb
  • tqdm
  • easydict
  • PyYAML
  • DeepRobust

Installation via PyPI

To install GSLB with pip, simply run:

pip install GSLB

Then, you can import GSL from your current environment.

Usage

If you want to quickly run an existing GSL model on a graph dataset:

python main.py --dataset dataset_name --model model_name -n num_trails -g 0 --use_knn -k 5 --use_mettack --sparse --metric acc -p 0. --drop_rate 0. --add_rate 0.

Optional arguments:

--dataset : the name of graph dataset

--model : the name of GSL model

--ntrail -n : repetition count of experiments

--use_knn : whether to use knn graph instead of the original graph

--k : the number of the nearest neighbors

--drop_rate : the probability of randomly edge deletion

--add_rate : the probability of randomly edge addition

--mask_feat_rate : the probability of randomly mask features

--use_mettack : whether to use the structure after being attacked by mettack

--ptb_rate -p : the perturbation rate

--metric : the evaluation metric

--gpu_num -g : the selected GPU number

Example: Train GRCN on Cora dataset, with the evaluation metric is accuracy.

python main.py --dataset cora --model GRCN --metric acc

If you want to quickly generate a perturbed graph by Mettack:

python generate_attack.py --dataset cora --ptb_rate 0.05

Step 1: Load datasets

from GSL.data import *

# load a homophilic or heterophilic graph dataset
data = Dataset(root='/tmp/', name='cora')

# load a perturbed graph dataset
data = Dataset(root='/tmp/', name='cora', use_mettack=True, ptb_rate=0.05)

# load a heterogeneous graph dataset
data = HeteroDataset(root='/tmp/', name='acm')

# load a graph-level dataset
data = GraphDataset(root='/tmp/', name='IMDB-BINARY', model='GCN')

Step 2: Initialize the GSL model

from GSL.model import *
from GSL.utils import accuracy, macro_f1, micro_f1

model_name = 'GRCN'
metric = 'acc'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# the hyper-parameters are recorded in config
config_path = './configs/{}_config.yaml'.format(model_name.lower())

# select a evaluation metric
eval_metric = {
  'acc': accuracy,
  'macro-f1': macro_f1,
  'micro-f1': micro_f1
}[metric]

model = GRCN(data.num_feat, data.num_class, evel_metric,
            config_path, dataset_name, device)

Step 3: Train GSL model

model.fit(data)

🧩 Implementation Algorithms

Currently, we have implemented the following GSL algorithms:

Algorithm Conference/Journal Category Paper Code
LDS ICML 2019 Homogeneous GSL Learning Discrete Structures for Graph Neural Networks Link
GRCN ECML PKDD 2020 Homogeneous GSL Graph-Revised Convolutional Network Link
ProGNN KDD 202 Homogeneous GSL Graph structure learning for robust graph neural networks Link
IDGL NeurIPS 2020 Homogeneous GSL Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings Link
GEN WWW 2021 Homogeneous GSL Graph Structure Estimation Neural Networks Link
CoGSL WWW 2022 Homogeneous GSL Compact Graph Structure Learning via Mutual Information Compression Link
SLAPS NeurIPS 2021 Homogeneous GSL SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks Link
SUBLIME WWW 2022 Homogeneous GSL Towards Unsupervised Deep Graph Structure Learning Link
STABLE KDD 2022 Homogeneous GSL Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN Link
NodeFormer NeurIPS 2022 Homogeneous GSL NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification Link
HES-GSL TNNLS 2023 Homogeneous GSL Homophily-Enhanced Self-Supervision for Graph Structure Learning: Insights and Directions Link
GSR WSDM 2023 Homogeneous GSL Self-Supervised Graph Structure Refinement for Graph Neural Networks Link
GTN NeurIPS 2020 Heterogeneous GSL Graph Transformer Networks Link
HGSL AAAI 2021 Heterogeneous GSL Heterogeneous Graph Structure Learning for Graph Neural Networks Link
HGP-SL AAAI 2020 Graph-level GSL Hierarchical Graph Pooling with Structure Learning Link
VIB-GSL AAAI 2022 Graph-level GSL Graph Structure Learning with Variational Information Bottleneck Link