Arms Race in Adversarial Graph Learning
Project description
⚔🛡 GraphWar: Arms Race in Graph Adversarial Attack and Defense
Graph War is Cooooooooming!!!!!
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 attacksare 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.
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