Defending graph neural networks against adversarial attacks (NeurIPS 2020)

Overview

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

Authors: Xiang Zhang ([email protected]), Marinka Zitnik ([email protected])

Project website

Overview

This repository contains python codes and datasets necessary to run the GNNGuard algorithm. GNNGuard is a general defense approach against a variety of poisoning adversarial attacks that perturb the discrete graph structure. GNNGuard can be straightforwardly incorporated into any GNN models to prevent the misclassification caused by poisoning adversarial attacks on graphs. Please see our paper for more details on the algorithm.

Key Idea of GNNGuard

Deep learning methods for graphs achieve remarkable performance on many tasks. However, despite the proliferation of such methods and their success, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest and most popular Graph Neural Networks (GNNs). By integrating with the proposed GNNGuard, the GNN classifier can correctly classify the target node even under strong adversarial attacks.

The key idea of GNNGuard is to detect and quantify the relationship between the graph structure and node features, if one exists, and then exploit that relationship to mitigate negative effects of the attack. GNNGuard learns how to best assign higher weights to edges connecting similar nodes while pruning edges between unrelated nodes. In specific, instead of the neural message passing of typical GNN (shown as A), GNNGuard (B) controls the message stream such as blocking the message from irrelevent neighbors but strengthening messages from highly-related ones. Importantly, we are the first model that can defend heterophily graphs (\eg, with structural equivalence) while all the existing defenders only considering homophily graphs.

Running the code

The GNNGuard is evluated under three typical adversarial attacks including Direct Targeted Attack (Nettack-Di), Influence Targeted Attack (Nettack-In), and Non-Targeted Attack (Mettack). In GNNGuard folder, the Nettack-Di.py, Nettack-In.py, and Mettack.py corresponding to the three adversarial attacks.

For example, to check the performance of GCN without defense under direct targeted attack, run the following code:

python Nettack-Di.py --dataset Cora  --modelname GCN --GNNGuard False

Turn on the GNNGuard defense, run

python Nettack-Di.py --dataset Cora  --modelname GCN --GNNGuard True

Note: Please uncomment the defense models (Line 144 for Nettack-Di.py) to test different defense models.

Citing

If you find GNNGuard useful for your research, please consider citing this paper:

@inproceedings{zhang2020gnnguard,
title     = {GNNGuard: Defending Graph Neural Networks against Adversarial Attacks},
author    = {Zhang, Xiang and Zitnik, Marinka},
booktitle = {NeurIPS},
year      = {2020}
}

Requirements

GNNGuard is tested to work under Python >=3.5.

Recent versions of Pytorch, torch-geometric, numpy, and scipy are required. All the required basic packages can be installed using the following command: ''' pip install -r requirements.txt ''' Note: For toch-geometric and the related dependices (e.g., cluster, scatter, sparse), the higher version may work but haven't been tested yet.

Install DeepRobust

During the evaluation, the adversarial attacks on graph are performed by DeepRobust from MSU, please install it by

git clone https://github.com/DSE-MSU/DeepRobust.git
cd DeepRobust
python setup.py install
  1. If you have trouble in installing DeepRobust, please try to replace the provided 'defense/setup.py' to replace the original DeepRobust-master/setup.py and manully reinstall it by
python setup.py install
  1. We extend the original DeepRobust from single GCN to multiplye GNN variants including GAT, GIN, Jumping Knowledge, and GCN-SAINT. After installing DeepRobust, please replace the origininal folder DeepRobust-master/deeprobust/graph/defense by the defense folder that provided in our repository!

  2. To better plugin GNNGuard to geometric codes, we slightly revised some functions in geometric. Please use the three files under our provided nn/conv/ to replace the corresponding files in the installed geometric folder (for example, the folder path could be /home/username/.local/lib/python3.5/site-packages/torch_geometric/nn/conv/).

Note: 1). Don't forget to backup all the original files when you replacing anything, in case you need them at other places! 2). Please install the corresponding CUDA versions if you are using GPU.

Datasets

Here we provide the datasets (including Cora, Citeseer, ogbn-arxiv, and DP) used in GNNGuard paper.

The ogbn-arxiv dataset can be easily access by python codes:

from ogb.nodeproppred import PygNodePropPredDataset
dataset = PygNodePropPredDataset(name = 'ogbn-arxiv')

More details about ogbn-arxiv dataset can be found here.

Find more details about Disease Pathway dataset at here.

For graphs with structural roles, a prominent type of heterophily, we calculate the nodes' similarity using graphlet degree vector instead of node embedding. The graphlet degree vector is generated/counted based on the Orbit Counting Algorithm (Orca).

Miscellaneous

Please send any questions you might have about the code and/or the algorithm to [email protected].

License

GNNGuard is licensed under the MIT License.

Owner
Zitnik Lab @ Harvard
Machine Learning for Medicine and Science
Zitnik Lab @ Harvard
UFPR-ADMR-v2 Dataset

UFPR-ADMR-v2 Dataset The UFPR-ADMRv2 dataset contains 5,000 dial meter images obtained on-site by employees of the Energy Company of Paraná (Copel), w

Gabriel Salomon 8 Sep 29, 2022
CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches This document describes how to install and use CRISCE (CRItical

Chair of Software Engineering II, Uni Passau 2 Feb 09, 2022
Real time sign language recognition

The proposed work aims at converting american sign language gestures into English that can be understood by everyone in real time.

Mohit Kaushik 6 Jun 13, 2022
PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

PyTorch Realtime Multi-Person Pose Estimation This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-P

Dave Fang 157 Nov 12, 2022
Catbird is an open source paraphrase generation toolkit based on PyTorch.

Catbird is an open source paraphrase generation toolkit based on PyTorch. Quick Start Requirements and Installation The project is based on PyTorch 1.

Afonso Salgado de Sousa 5 Dec 15, 2022
PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"

R2Plus1D-PyTorch PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal

Irhum Shafkat 342 Dec 16, 2022
Massively parallel Monte Carlo diffusion MR simulator written in Python.

Disimpy Disimpy is a Python package for generating simulated diffusion-weighted MR signals that can be useful in the development and validation of dat

Leevi 16 Nov 11, 2022
Implementation of the Point Transformer layer, in Pytorch

Point Transformer - Pytorch Implementation of the Point Transformer self-attention layer, in Pytorch. The simple circuit above seemed to have allowed

Phil Wang 501 Jan 03, 2023
Detectron2 for Document Layout Analysis

Detectron2 trained on PubLayNet dataset This repo contains the training configurations, code and trained models trained on PubLayNet dataset using Det

Himanshu 163 Nov 21, 2022
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Hugging Face 77.2k Jan 02, 2023
Convert openmmlab (not only mmdetection) series model to tensorrt

MMDet to TensorRT This project aims to convert the mmdetection model to TensorRT model end2end. Focus on object detection for now. Mask support is exp

JinTian 4 Dec 17, 2021
An official TensorFlow implementation of “CLCC: Contrastive Learning for Color Constancy” accepted at CVPR 2021.

CLCC: Contrastive Learning for Color Constancy (CVPR 2021) Yi-Chen Lo*, Chia-Che Chang*, Hsuan-Chao Chiu, Yu-Hao Huang, Chia-Ping Chen, Yu-Lin Chang,

Yi-Chen (Howard) Lo 58 Dec 17, 2022
A simple Rock-Paper-Scissors game using CV in python

ML18_Rock-Paper-Scissors-using-CV A simple Rock-Paper-Scissors game using CV in python For IITISOC-21 Rules and procedure to play the interactive game

Anirudha Bhagwat 3 Aug 08, 2021
ByteTrack with ReID module following the paradigm of FairMOT, tracking strategy is borrowed from FairMOT/JDE.

ByteTrack_ReID ByteTrack is the SOTA tracker in MOT benchmarks with strong detector YOLOX and a simple association strategy only based on motion infor

Han GuangXin 46 Dec 29, 2022
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Core ML Tools Use coremltools to convert machine learning models from third-party libraries to the Core ML format. The Python package contains the sup

Apple 3k Jan 08, 2023
[Machine Learning Engineer Basic Guide] 부스트캠프 AI Tech - Product Serving 자료

Boostcamp-AI-Tech-Product-Serving 부스트캠프 AI Tech - Product Serving 자료 Repository 구조 part1(MLOps 개론, Model Serving, 머신러닝 프로젝트 라이프 사이클은 별도의 코드가 없으며, part

Sung Yun Byeon 269 Dec 21, 2022
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Jan 05, 2023
A Python library created to assist programmers with complex mathematical functions

libmaths libmaths was created not only as a learning experience for me, but as a way to make mathematical models in seconds for Python users using mat

Simple 73 Oct 02, 2022
Character Controllers using Motion VAEs

Character Controllers using Motion VAEs This repo is the codebase for the SIGGRAPH 2020 paper with the title above. Please find the paper and demo at

Electronic Arts 165 Jan 03, 2023
This is the latest version of the PULP SDK

PULP-SDK This is the latest version of the PULP SDK, which is under active development. The previous (now legacy) version, which is no longer supporte

78 Dec 07, 2022