A graph adversarial learning toolbox based on PyTorch and DGL.

Last update: Jan 05, 2023

Related tags

Deep Learning GraphWar

Overview

GraphWar: Arms Race in Graph Adversarial Learning

NOTE: GraphWar is still in the early stages and the API will likely continue to change.

🚀 Installation

Please make sure you have installed PyTorch and Deep Graph Library(DGL).

# Comming soon
pip install -U graphwar

# 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 dgl.DGLgraph instance g that describes you dataset.

A simple targeted attack

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

A simple untargeted attack

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

Implementations

In detail, the following methods are currently implemented:

Attack

Targeted Attack

Methods	Venue
RandomAttack	A simple random method that chooses edges to flip randomly.
DICEAttack	Marcin Waniek et al. 📝 Hiding Individuals and Communities in a Social Network, Nature Human Behavior'16
Nettack	Daniel Zügner et al. 📝 Adversarial Attacks on Neural Networks for Graph Data, KDD'18
FGAttack	Jinyin Chen et al. 📝 Fast Gradient Attack on Network Embedding, arXiv'18 Jinyin Chen et al. 📝 Link Prediction Adversarial Attack Via Iterative Gradient Attack, IEEE Trans'20 Hanjun Dai et al. 📝 Adversarial Attack on Graph Structured Data, ICML'18
GFAttack	Heng Chang et al. 📝 A Restricted Black - box Adversarial Framework Towards Attacking Graph Embedding Models, AAAI'20
IGAttack	Huijun Wu et al. 📝 Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, IJCAI'19
SGAttack	Jintang Li et al. 📝 Adversarial Attack on Large Scale Graph, TKDE'21

Untargeted Attack

Methods	Venue
RandomAttack	A simple random method that chooses edges to flip randomly
DICEAttack	Marcin Waniek et al. 📝 Hiding Individuals and Communities in a Social Network, Nature Human Behavior'16
FGAttack	Jinyin Chen et al. 📝 Fast Gradient Attack on Network Embedding, arXiv'18 Jinyin Chen et al. 📝 Link Prediction Adversarial Attack Via Iterative Gradient Attack, IEEE Trans'20 Hanjun Dai et al. 📝 Adversarial Attack on Graph Structured Data, ICML'18
Metattack	Daniel Zügner et al. 📝 Adversarial Attacks on Graph Neural Networks via Meta Learning, ICLR'19
PGD, MinmaxAttack	Kaidi Xu et al. 📝 Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective, IJCAI'19

Defense

Model-Level

Methods	Venue
MedianGCN	Liang Chen et al. 📝 Understanding Structural Vulnerability in Graph Convolutional Networks, IJCAI'21
RobustGCN	Dingyuan Zhu et al. 📝 Robust Graph Convolutional Networks Against Adversarial Attacks, KDD'19

Data-Level

Methods	Venue
JaccardPurification	Huijun Wu et al. 📝 Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, IJCAI'19

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

Comments

Benchmark Results of Attack Performance

Hi, thanks for sharing the awesome repo with us! I recently run the attack sample code but the resultpgd_attack.py and random_attack.py under examples/attack/untargeted, but the accuracies of both evasion and poison attack seem not to decrease.

I'm pretty confused by the attack results. For CV models, pgd attack easily decreases the accuracy to nearly random guesses, but the results of GreatX seem not to consent with CV models. Is it because the number of the perturbed edges is too small?

Here are the results of pgd_attack.py

Processing...
Done!
Training...
100/100 [==============================] - Total: 874.37ms - 8ms/step- loss: 0.0524 - acc: 0.996 - val_loss: 0.625 - val_acc: 0.815
Evaluating...
1/1 [==============================] - Total: 1.82ms - 1ms/step- loss: 0.597 - acc: 0.843
Before attack
 Objects in BunchDict:
╒═════════╤═══════════╕
│ Names   │   Objects │
╞═════════╪═══════════╡
│ loss    │  0.59718  │
├─────────┼───────────┤
│ acc     │  0.842555 │
╘═════════╧═══════════╛
PGD training...: 100%|███████████████████████████████████████████████████████████████████████| 200/200 [00:02<00:00, 69.74it/s]
Bernoulli sampling...: 100%|██████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 804.86it/s]
Evaluating...
1/1 [==============================] - Total: 2.11ms - 2ms/step- loss: 0.603 - acc: 0.842
After evasion attack
 Objects in BunchDict:
╒═════════╤═══════════╕
│ Names   │   Objects │
╞═════════╪═══════════╡
│ loss    │  0.603293 │
├─────────┼───────────┤
│ acc     │  0.842052 │
╘═════════╧═══════════╛
Training...
100/100 [==============================] - Total: 535.83ms - 5ms/step- loss: 0.124 - acc: 0.976 - val_loss: 0.728 - val_acc: 0.779
Evaluating...
1/1 [==============================] - Total: 1.74ms - 1ms/step- loss: 0.766 - acc: 0.827
After poisoning attack
 Objects in BunchDict:
╒═════════╤═══════════╕
│ Names   │   Objects │
╞═════════╪═══════════╡
│ loss    │  0.76604  │
├─────────┼───────────┤
│ acc     │  0.826962 │
╘═════════╧═══════════╛

Here are the results of random_attack.py

Training...
100/100 [==============================] - Total: 600.92ms - 6ms/step- loss: 0.0615 - acc: 0.984 - val_loss: 0.626 - val_acc: 0.811
Evaluating...
1/1 [==============================] - Total: 1.93ms - 1ms/step- loss: 0.564 - acc: 0.832
Before attack
 Objects in BunchDict:
╒═════════╤═══════════╕
│ Names   │   Objects │
╞═════════╪═══════════╡
│ loss    │  0.564449 │
├─────────┼───────────┤
│ acc     │  0.832495 │
╘═════════╧═══════════╛
Peturbing graph...: 253it [00:00, 4588.44it/s]
Evaluating...
1/1 [==============================] - Total: 2.14ms - 2ms/step- loss: 0.585 - acc: 0.826
After evasion attack
 Objects in BunchDict:
╒═════════╤═══════════╕
│ Names   │   Objects │
╞═════════╪═══════════╡
│ loss    │  0.584646 │
├─────────┼───────────┤
│ acc     │  0.826459 │
╘═════════╧═══════════╛
Training...
100/100 [==============================] - Total: 530.04ms - 5ms/step- loss: 0.0767 - acc: 0.98 - val_loss: 0.574 - val_acc: 0.791
Evaluating...
1/1 [==============================] - Total: 1.77ms - 1ms/step- loss: 0.695 - acc: 0.813
After poisoning attack
 Objects in BunchDict:
╒═════════╤═══════════╕
│ Names   │   Objects │
╞═════════╪═══════════╡
│ loss    │  0.695349 │
├─────────┼───────────┤
│ acc     │  0.81338  │
╘═════════╧═══════════╛

opened by ziqi-zhang 2

SG Attack example cannot run as expected on cuda
Hello, I got some error when I run SG Attack's example code on cuda device:

Traceback (most recent call last): File "src/test.py", line 50, in <module> attacker.attack(target) File "/greatx/attack/targeted/sg_attack.py", line 212, in attack subgraph = self.get_subgraph(target, target_label, best_wrong_label) File "/greatx/attack/targeted/sg_attack.py", line 124, in get_subgraph self.label == best_wrong_label)[0].cpu().numpy() RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:3 and cpu!

I found self.label is on cuda device, but best_wrong_label is on cpu. https://github.com/EdisonLeeeee/GreatX/blob/73eac351fdae842dbd74967622bd0e573194c765/greatx/attack/targeted/sg_attack.py#L123-L124

I remove line94 .cpu(), everything is going well and no error report

https://github.com/EdisonLeeeee/GreatX/blob/73eac351fdae842dbd74967622bd0e573194c765/greatx/attack/targeted/sg_attack.py#L94-L96

I found there is a commit that adds .cpu end of line 94, so I dont know it's a bug or something else🤨
opened by beiyanpiki 1
problem with metattack

Thanks for this wonderful repo. However, when I run the metattack example ,the result is not promising Here is my result when attack Cora with metattack Training... 100/100 [====================] - Total: 520.68ms - 5ms/step- loss: 0.0713 - acc: 0.996 - val_loss: 0.574 - val_acc: 0.847 Evaluating... 1/1 [====================] - Total: 2.01ms - 2ms/step- loss: 0.522 - acc: 0.847 Before attack ╒═════════╤═══════════╕ │ Names │ Objects │ ╞═════════╪═══════════╡ │ loss │ 0.521524 │ ├─────────┼───────────┤ │ acc │ 0.846579 │ ╘═════════╧═══════════╛ Peturbing graph...: 100%|██████████| 253/253 [01:00<00:00, 4.17it/s]Evaluating... 1/1 [====================] - Total: 2.08ms - 2ms/step- loss: 0.528 - acc: 0.844 After evasion attack ╒═════════╤═══════════╕ │ Names │ Objects │ ╞═════════╪═══════════╡ │ loss │ 0.528431 │ ├─────────┼───────────┤ │ acc │ 0.844064 │ ╘═════════╧═══════════╛ Training... 32/100 [=====>..............] - ETA: 0s- loss: 0.212 - acc: 0.956 - val_loss: 0.634 - val_acc: 0.807 100/100 [====================] - Total: 407.58ms - 4ms/step- loss: 0.0601 - acc: 0.996 - val_loss: 0.704 - val_acc: 0.787 Evaluating... 1/1 [====================] - Total: 1.66ms - 1ms/step- loss: 0.711 - acc: 0.819 After poisoning attack ╒═════════╤═══════════╕ │ Names │ Objects │ ╞═════════╪═══════════╡ │ loss │ 0.710625 │ ├─────────┼───────────┤ │ acc │ 0.818913 │ ╘═════════╧═══════════╛

opened by shanzhiq 1
Fix a venue of FeaturePropagation

Rossi's FeaturePropagation paper was submitted to ICLR'21 but was rejected. So how about updating with arXiv? This paper has not yet been accepted by other conferences.
bug documentation

opened by jeongwhanchoi 1
Add ratio for `attacker.data()`, `attacker.edge_flips()`, and `attacker.feat_flips()`
This PR allows specifying an argument ratio for attacker.edge_flips(), and attacker.feat_flips(), which determines how many generated perturbations were used for further evaluation/visualization. Correspondingly, attacker.data() holds edge_ratio and feat_ratio for these two methods when constructing perturbed graph.

Example

attacker = ... attacker.reset() attacker.attack(...) # Case1: Only 50% of generated edge perturbations were used trainer.evaluate(attacker.data(edge_ratio=0.5), mask=...) # Case2: Only 50% of generated feature perturbations were used trainer.evaluate(attacker.data(feat_ratio=0.5), mask=...) # NOTE: both arguments can be used simultaneously
enhancement
opened by EdisonLeeeee 0

Releases(0.1.0)

0.1.0(Jun 9, 2022)
GraphWar 0.1.0 🎉

The first major release, built upon PyTorch and PyTorch Geometric (PyG).

About GraphWar

GraphWar is a graph adversarial learning toolbox based on PyTorch and PyTorch Geometric (PyG). It implements a wide range of adversarial attacks and defense methods focused on graph data. To facilitate the benchmark evaluation on graphs, we also provide a set of implementations on popular Graph Neural Networks (GNNs).

Usages

For more details, please refer to the documentation and examples.

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()

We will continue to develop this project and introduce more state-of-the-art implementations of papers in the field of graph adversarial attacks and defenses.
Source code(tar.gz)
Source code(zip)
graphwar-0.1.0-py3-none-any.whl(155.84 KB)

Owner

Jintang Li

Ph.D. student @ Sun Yat-sen University (SYSU), China.

GitHub Repository

Optimus: the first large-scale pre-trained VAE language model

Optimus: the first pre-trained Big VAE language model This repository contains source code necessary to reproduce the results presented in the EMNLP 2

314 Dec 19, 2022

Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"

LQVAE-separation Code for "Unsupervised Source Separation via Bayesian inference in the latent domain" Paper Samples GT Compressed Separated Drums GT

30 Oct 25, 2022

Convolutional neural network web app trained to track our infant’s sleep schedule using our Google Nest camera.

Machine Learning Sleep Schedule Tracker What is it? Convolutional neural network web app trained to track our infant’s sleep schedule using our Google

7 Jul 15, 2022

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Men

4 Dec 27, 2022

GRF: Learning a General Radiance Field for 3D Representation and Rendering

GRF: Learning a General Radiance Field for 3D Representation and Rendering [Paper] [Video] GRF: Learning a General Radiance Field for 3D Representatio

243 Dec 29, 2022

Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

16 Jul 18, 2022

Text Summarization - WCN — Weighted Contextual N-gram method for evaluation of Text Summarization

Text Summarization WCN — Weighted Contextual N-gram method for evaluation of Text Summarization In this project, I fine tune T5 model on Extreme Summa

1 Jan 03, 2022

WormMovementSimulation - 3D Simulation of Worm Body Movement with Neurons attached to its body

Generate 3D Locomotion Data This module is intended to create 2D video trajector

1 Aug 09, 2022

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

2.8k Dec 26, 2022

Spatial color quantization in Rust

rscolorq Rust port of Derrick Coetzee's scolorq, based on the 1998 paper "On spatial quantization of color images" by Jan Puzicha, Markus Held, Jens K

37 Dec 22, 2022

BARTScore: Evaluating Generated Text as Text Generation

This is the Repo for the paper: BARTScore: Evaluating Generated Text as Text Generation Updates 2021.06.28 Release online evaluation Demo 2021.06.25 R

196 Dec 17, 2022

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

IsoTree Fast and multi-threaded implementation of Extended Isolation Forest, Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and regular

141 Dec 29, 2022

A graph adversarial learning toolbox based on PyTorch and DGL.

Related tags

Overview

GraphWar: Arms Race in Graph Adversarial Learning

🚀 Installation

Get Started

A simple targeted attack

A simple untargeted attack

Implementations

Attack

Targeted Attack

Untargeted Attack

Defense

Model-Level

Data-Level

Comments

Benchmark Results of Attack Performance

SG Attack example cannot run as expected on cuda

problem with metattack

Fix a venue of FeaturePropagation

Add ratio for `attacker.data()`, `attacker.edge_flips()`, and `attacker.feat_flips()`

Releases(0.1.0)

0.1.0(Jun 9, 2022)

GraphWar 0.1.0 🎉

About GraphWar

Usages

How fast can we train and evaluate your own GNN?

A simple targeted manipulation attack

A simple untargeted (non-targeted) manipulation attack

Owner

Jintang Li

Optimus: the first large-scale pre-trained VAE language model

Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"

Convolutional neural network web app trained to track our infant’s sleep schedule using our Google Nest camera.

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

GRF: Learning a General Radiance Field for 3D Representation and Rendering

Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Text Summarization - WCN — Weighted Contextual N-gram method for evaluation of Text Summarization

WormMovementSimulation - 3D Simulation of Worm Body Movement with Neurons attached to its body

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

Spatial color quantization in Rust

BARTScore: Evaluating Generated Text as Text Generation

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

Repository containing detailed experiments related to the paper "Memotion Analysis through the Lens of Joint Embedding".

A curated list of awesome neural radiance fields papers

BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization

Code of Periodic Activation Functions Induce Stationarity

High-quality implementations of standard and SOTA methods on a variety of tasks.

Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.

Predict the latency time of the deep learning models

This repository contains implementations and illustrative code to accompany DeepMind publications