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Structack: Structure-based Adversarial Attacks on Graph Neural Networks
Structure-based, uninformed adversarial attacks on graph neural networks

Hussain Hussain, Tomislav Duricic, Elisabeth Lex, Denis Helic, Markus Strohmaier, Roman Kern

15 September 2026, 23:12
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Source and license. “Structack: Structure-based Adversarial Attacks on Graph Neural Networks” by Hussain Hussain, Tomislav Duricic, Elisabeth Lex, Denis Helic, Markus Strohmaier, and Roman Kern, Proceedings of the 32nd ACM Conference on Hypertext and Social Media (HT ’21) (2021), DOI: 10.1145/3465336.3475110. Source PDF: 3465336.3475110.pdf; version-of-record landing page: https://dl.acm.org/doi/10.1145/3465336.3475110. Licensed under CC BY 4.0. Changes: faithful page-image reproduction from the supplied PDF; no substantive changes are intended.

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Attributes
doi
10.1145/3465336.3475110
name
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
pages
11
venue
Proceedings of the 32nd ACM Conference on Hypertext and Social Media (HT ’21)
acm_url
https://dl.acm.org/doi/10.1145/3465336.3475110
authors
1.Hussain Hussain
2.Tomislav Duricic
3.Elisabeth Lex
4.Denis Helic
5.Markus Strohmaier
6.Roman Kern
doi_url
https://doi.org/10.1145/3465336.3475110
license
This work is licensed under a Creative Commons Attribution International 4.0 License.
summary
Structure-based, uninformed adversarial attacks on graph neural networks
keywords
Graph neural networks; adversarial attacks; network centrality;
source_pdf
3465336.3475110.pdf
import_kind
full_text
open_access
false
ccs_concepts
· Computing methodologies →Machine learning; Adversar-
displayAuthor
Hussain Hussain, Tomislav Duricic, Elisabeth Lex, Denis Helic, Markus Strohmaier, Roman Kern
source_sha256
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publication_year
2021