ICLR 2025

Published as a conference paper at ICLR 2025
GOTTACK: UNIVERSAL ADVERSARIAL ATTACKS ON
GRAPH NEURAL NETWORKS VIA GRAPH ORBITS
LEARNING
Zulfikar Alom1,
Tran Gia Bao Ngo1,
Murat Kantarcioglu2,
Cuneyt Gurcan Akcora3
1Department of Computer Science, University of Manitoba, Canada
2Department of Computer Science, Virginia Tech, USA
3AI Initiative - University of Central Florida, USA
md.alom@umanitoba.ca,
ngot1@myumanitoba.ca,
muratk@vt.edu
cuneyt.akcora@ucf.edu
ABSTRACT
Graph Neural Networks (GNNs) have demonstrated superior performance in node
classification tasks across diverse applications. However, their vulnerability to
adversarial attacks, where minor perturbations can mislead model predictions,
poses significant challenges. This study introduces GOttack, a novel adversarial
attack framework that exploits the topological structure of graphs to undermine the
integrity of GNN predictions systematically.
By defining a topology-aware method to manipulate graph orbits, our approach
generates adversarial modifications that are both subtle and effective, posing a
severe test to the robustness of GNNs. We evaluate the efficacy of GOttack
across multiple prominent GNN architectures using standard benchmark datasets.
Our results show that GOttack outperforms existing state-of-the-art adversarial
techniques and completes training in approximately 55% of the time required by
the fastest competing model, achieving the highest average misclassification rate
in 155 tasks. This work not only sheds light on the susceptibility of GNNs to
structured adversarial attacks but also shows that certain topological patterns may
play a significant role in the underlying robustness of the GNNs. Our Python
implementation is shared at https://github.com/cakcora/GOttack.
1
INTRODUCTION
Recent advances in Graph Neural Networks (GNNs) have brought significant progress in node classifi-
cation tasks, utilizing the power of graph topology and node features to generate insightful inferences
across va...