DeepAI AI Chat
Log In Sign Up

Task and Model Agnostic Adversarial Attack on Graph Neural Networks

by   Kartik Sharma, et al.

Graph neural networks (GNNs) have witnessed significant adoption in the industry owing to impressive performance on various predictive tasks. Performance alone, however, is not enough. Any widely deployed machine learning algorithm must be robust to adversarial attacks. In this work, we investigate this aspect for GNNs, identify vulnerabilities, and link them to graph properties that may potentially lead to the development of more secure and robust GNNs. Specifically, we formulate the problem of task and model agnostic evasion attacks where adversaries modify the test graph to affect the performance of any unknown downstream task. The proposed algorithm, GRAND (Graph Attack via Neighborhood Distortion) shows that distortion of node neighborhoods is effective in drastically compromising prediction performance. Although neighborhood distortion is an NP-hard problem, GRAND designs an effective heuristic through a novel combination of Graph Isomorphism Network with deep Q-learning. Extensive experiments on real datasets show that, on average, GRAND is up to 50% more effective than state of the art techniques, while being more than 100 times faster.


page 1

page 2

page 3

page 4


Unnoticeable Backdoor Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have achieved promising results in various ...

TDGIA:Effective Injection Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have achieved promising performance in vari...

Jointly Attacking Graph Neural Network and its Explanations

Graph Neural Networks (GNNs) have boosted the performance for many graph...

GUAP: Graph Universal Attack Through Adversarial Patching

Graph neural networks (GNNs) are a class of effective deep learning mode...

GraphSeam: Supervised Graph Learning Framework for Semantic UV Mapping

Recently there has been a significant effort to automate UV mapping, the...

Stealing Links from Graph Neural Networks

Graph data, such as social networks and chemical networks, contains a we...

Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers

Graph neural networks (GNNs) have achieved high performance in analyzing...