Structack: Structure-based Adversarial Attacks on Graph Neural Networks

Hussain, Hussain; Đuričić, Tomislav; Lex, Elisabeth; Helić, Denis; Strohmaier, Markus; Kern, Roman

doi:10.1145/3465336.3475110

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Additionally, we found that connecting two nodes with low node centrality (in terms of degree and PageRank centrality) is also an effective attack. This demonstrates that low-degree attacks [42,43], which are effective in static graphs, also exhibit partial effectiveness in dynamic graphs.…”

Section: Attack Performancementioning

confidence: 85%

A Study on the Curriculum of the Graduate School of Department of Education Consulting in South Korea

Lee¹

2022

Korea Assoc Edu Consulting Coaching

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The purpose of this study was to suggest implications for the curriculum of the graduate school of department of education consulting and education consulting major in South Korea through the analysis of the current status and curriculum of the graduate school of department of education consulting and education consulting major in South Korea. In this study, in order to investigate the current status and curriculum of the graduate school of department of education consulting and education consulting major in South Korea, the keywords were limited to ‘education consulting’, and data were collected in the order of Daehakalrimi(https://www.academyinfo.go.kr), Naver and Google keyword search, and each university and graduate school’s website. As a result of the analysis of the collected data, it was confirmed that three master’s and doctoral programs in general graduate schools in South Korea, and nine master’s programs in graduate schools of education in South Korea were in operation, and the curriculum of the graduate school of department of education consulting and education consulting major in South Korea were organized based on the faculty members’s major and expertise according to new department and major or name changed department and major. This study is the first study to analysis the current status and curriculum of the graduate school of department of education consulting and education consulting major in South Korea and is meaningful as a basic study of the development of education consulting as disciplines.

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Section: Attack Performancementioning

confidence: 85%

A Study on the Curriculum of the Graduate School of Department of Education Consulting in South Korea

Lee¹

2022

Korea Assoc Edu Consulting Coaching

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“…As the perturbations generated by our attack are the least likely edges to be formed when considering the graphs' dynamics, these perturbations effectively Additionally, we found that connecting two nodes with low node centrality (in terms of degree and PageRank centrality) is also an effective attack. This demonstrates that low-degree attacks (Hussain et al 2021;Ma, Ding, and Mei 2020), which are effective in static graphs, also exhibit partial effectiveness in dynamic graphs.…”

Section: Attack Performancementioning

confidence: 88%

Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs

Lee,

Shin

2024

AAAI

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Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been developed to effectively capture the evolving patterns in dynamic graphs. While these models have demonstrated their superiority, being widely adopted in various important fields, their vulnerabilities against adversarial attacks remain largely unexplored. In this paper, we propose T-SPEAR, a simple and effective adversarial attack method for link prediction on continuous-time dynamic graphs, focusing on investigating the vulnerabilities of TGNNs. Specifically, before the training procedure of a victim model, which is a TGNN for link prediction, we inject edge perturbations to the data that are unnoticeable in terms of the four constraints we propose, and yet effective enough to cause malfunction of the victim model. Moreover, we propose a robust training approach T-SHIELD to mitigate the impact of adversarial attacks. By using edge filtering and enforcing temporal smoothness to node embeddings, we enhance the robustness of the victim model. Our experimental study shows that T-SPEAR significantly degrades the victim model's performance on link prediction tasks, and even more, our attacks are transferable to other TGNNs, which differ from the victim model assumed by the attacker. Moreover, we demonstrate that T-SHIELD effectively filters out adversarial edges and exhibits robustness against adversarial attacks, surpassing the link prediction performance of the naive TGNN by up to 11.2% under T-SPEAR. The code and datasets are available at https://github.com/wooner49/T-spear-shield

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“…Topology Attacks. With the wide applications of GNNs, their robustness to adversarial attacks has received increasing attention, especially for topology attacks [5,7,9,11,21,22,29,33,38,39,42,42,47]. They try to find the most adversarial perturbations within a fixed budget Δ.…”

Section: Related Workmentioning

confidence: 99%

Minimum Topology Attacks for Graph Neural Networks

Zhang

Wang

Shi

et al. 2023

Proceedings of the ACM Web Conference 2023

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With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have been proposed, they mainly focus on fixed-budget attacks, aiming at finding the most adversarial perturbations within a fixed budget for target node. However, considering the varied robustness of each node, there is an inevitable dilemma caused by the fixed budget, i.e., no successful perturbation is found when the budget is relatively small, while if it is too large, the yielding redundant perturbations will hurt the invisibility. To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node. To this end, we propose an attack model, named MiBTack, based on a dynamic projected gradient descent algorithm, which can effectively solve the involving non-convex constraint optimization on discrete topology. Extensive results on three GNNs and four real-world datasets show that MiBTack can successfully lead all target nodes misclassified with the minimum perturbation edges. Moreover, the obtained minimum budget can be used to measure node robustness, so we can explore the relationships of robustness, topology, and uncertainty for nodes, which is beyond what the current fixed-budget topology attacks can offer. CCS CONCEPTS• Computer systems organization → Embedded systems; • Networks → Network reliability.

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Structack: Structure-based Adversarial Attacks on Graph Neural Networks

Cited by 8 publications

References 20 publications

A Study on the Curriculum of the Graduate School of Department of Education Consulting in South Korea

A Study on the Curriculum of the Graduate School of Department of Education Consulting in South Korea

Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs

Minimum Topology Attacks for Graph Neural Networks

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