graphwar 0.1.0

Arms Race in Adversarial Graph Learning

⚔🛡 GraphWar: Arms Race in Graph Adversarial Attack and Defense

Graph War is Cooooooooming!!!!!

[Documentation] | [Examples]

Know thy self, know thy enemy. A thousand battles, a thousand victories.

「知己知彼，百战百胜」 ——《孙子兵法•谋攻篇》

💨 News

• May 27, 2022: GraphWar has been refactored with PyTorch Geometric (PyG), old code based on DGL can be found here. We will soon release the first version of GraphWar, stay tuned!

NOTE: GraphWar is still in the early stages and the API will likely continue to change. If you are interested in this project, don't hesitate to contact me or make a PR directly.

🚀 Installation

Please make sure you have installed PyTorch and PyTorch Geometric (PyG).

# Coming soon
pip install -U graphwar

or

# Recommended
git clone https://github.com/EdisonLeeeee/GraphWar.git && cd GraphWar
pip install -e . --verbose

where -e means "editable" mode so you don't have to reinstall every time you make changes.

⚡ Get Started

Assume that you have a torch_geometric.data.Data instance data that describes your graph.

How fast can we train and evaluate your own GNN?

Take GCN as an example:

from graphwar.nn.models import GCN
from graphwar.training import Trainer
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root='.', name='Cora') # Any PyG dataset is available!
data = dataset[0]
model = GCN(dataset.num_features, dataset.num_classes)
trainer = Trainer(model, device='cuda:0')
trainer.fit({'data': data, 'mask': data.train_mask})
trainer.evaluate({'data': data, 'mask': data.test_mask})

A simple targeted manipulation attack

from graphwar.attack.targeted import RandomAttack
attacker = RandomAttack(data)
attacker.attack(1, num_budgets=3) # attacking target node `1` with `3` edges 
attacked_data = attacker.data()
edge_flips = attacker.edge_flips()

A simple untargeted (non-targeted) manipulation attack

from graphwar.attack.untargeted import RandomAttack
attacker = RandomAttack(data)
attacker.attack(num_budgets=0.05) # attacking the graph with 5% edges perturbations
attacked_data = attacker.data()
edge_flips = attacker.edge_flips()

👀 Implementations

In detail, the following methods are currently implemented:

⚔ Attack

Graph Manipulation Attack (GMA)

Targeted Attack

Methods	Descriptions	Examples
RandomAttack	A simple random method that chooses edges to flip randomly.	[Example]
DICEAttack	Waniek et al. Hiding Individuals and Communities in a Social Network, Nature Human Behavior'16	[Example]
Nettack	Zügner et al. Adversarial Attacks on Neural Networks for Graph Data, KDD'18	[Example]
FGAttack	Chen et al. Fast Gradient Attack on Network Embedding, arXiv'18	[Example]
GFAttack	Chang et al. A Restricted Black - box Adversarial Framework Towards Attacking Graph Embedding Models, AAAI'20	[Example]
IGAttack	Wu et al. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, IJCAI'19	[Example]
SGAttack	Li et al. Adversarial Attack on Large Scale Graph, TKDE'21	[Example]

Untargeted Attack

Methods	Descriptions	Examples
RandomAttack	A simple random method that chooses edges to flip randomly	[Example]
DICEAttack	Waniek et al. Hiding Individuals and Communities in a Social Network, Nature Human Behavior'16	[Example]
FGAttack	Chen et al. Fast Gradient Attack on Network Embedding, arXiv'18	[Example]
Metattack	Zügner et al. Adversarial Attacks on Graph Neural Networks via Meta Learning, ICLR'19	[Example]
IGAttack	Wu et al. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, IJCAI'19	[Example]
PGD	Xu et al. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective, IJCAI'19	[Example]
MinmaxAttack	Xu et al. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective, IJCAI'19	[Example]

Graph Injection Attack (GIA)

Methods	Descriptions	Examples
RandomInjection	A simple random method that chooses nodes to inject randomly.	[Example]
AdvInjection	The 2nd place solution of KDD Cup 2020, team: ADVERSARIES.	[Example]

Graph Universal Attack (GUA)

Graph Backdoor Attack (GBA)

Methods	Descriptions	Examples
LGCBackdoor	Chen et al. Neighboring Backdoor Attacks on Graph Convolutional Network, arXiv'22	[Example]
FGBackdoor	Chen et al. Neighboring Backdoor Attacks on Graph Convolutional Network, arXiv'22	[Example]

🛡 Defense

Standard GNNs (without defense)

Methods	Descriptions	Examples
GCN	Kipf et al. Semi-Supervised Classification with Graph Convolutional Networks, ICLR'17	[Example]
SGC	Wu et al. Simplifying Graph Convolutional Networks, ICLR'19	[Example]
GAT	Veličković et al. Graph Attention Networks, ICLR'18	[Example]
DAGNN	Liu et al. Towards Deeper Graph Neural Networks, KDD'20	[Example]
APPNP	Klicpera et al. Predict then Propagate: Graph Neural Networks meet Personalized PageRank, ICLR'19	[Example]
JKNet	Xu et al. Representation Learning on Graphs with Jumping Knowledge Networks, ICML'18	[Example]
TAGCN	Du et al. Topological Adaptive Graph Convolutional Networks, arXiv'17	[Example]
SSGC	ZHu et al. Simple Spectral Graph Convolution, ICLR'21	[Example]

Robust GNNs

Methods	Descriptions	Examples
MedianGCN	Chen et al. Understanding Structural Vulnerability in Graph Convolutional Networks, IJCAI'21	[Example]
RobustGCN	Zhu et al. Robust Graph Convolutional Networks Against Adversarial Attacks, KDD'19	[Example]
SoftMedianGCN	Geisler et al. Reliable Graph Neural Networks via Robust Aggregation, NeurIPS'20 Geisler et al. Robustness of Graph Neural Networks at Scale, NeurIPS'21	[Example]
ElasticGNN	Liu et al. Elastic Graph Neural Networks, ICML'21	[Example]
AirGNN	Liu et al. Graph Neural Networks with Adaptive Residual, NeurIPS'21	[Example]
SimPGCN	Jin et al. Node Similarity Preserving Graph Convolutional Networks, WSDM'21	[Example]
SAT	Li et al. Spectral Adversarial Training for Robust Graph Neural Network, arXiv'22	[Example]

Defense Strategy

Methods	Descriptions	Examples
JaccardPurification	Wu et al. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, IJCAI'19	[Example]
SVDPurification	Entezari et al. All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs, WSDM'20	[Example]
GNNGUARD	Zhang et al. GNNGUARD: Defending Graph Neural Networks against Adversarial Attacks, NeurIPS'20	[Example]
GUARD	Li et al. GUARD: Graph Universal Adversarial Defense, arXiv'22	[Example]

More details of literatures and the official codes can be found at Awesome Graph Adversarial Learning.

Others

Methods	Descriptions	Examples
DropEdge	Rong et al. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification, ICLR'20	[Example]
DropNode	You et al. Graph Contrastive Learning with Augmentations, NeurIPS'20	[Example]
DropPath	Li et al. MaskGAE: Masked Graph Modeling Meets Graph Autoencoders, arXiv'22'	[Example]
Centered Kernel Alignment (CKA)	Nguyen et al. Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth, ICLR'21	[Example]

❓ Known Issues

• Despite our best efforts, we still had difficulty reproducing the results of GNNGUARD in the paper. If you find any problems, please don't hesitate to contact me.
• Untargeted attacks are suffering from performance degradation, as also in DeepRobust, when a validation set is used during training with model picking. Such phenomenon has also been revealed in Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense.