Mar 5, 2023 · Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation. Authors:Yixin Liu, Chenrui Fan, Pan Zhou, Lichao Sun.
The first step in safeguarding personal graph data from being exploited by GNN models. DEFEND CAPACITY. The Min-Min Optimization. • We suppress the gradient ...
Mar 5, 2023 · vulnerability of unauthorized graph exploitation. As far as we know ... Our method explores invisible noise to prevent GNN models from exploiting ...
Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through Data-Free Model Extraction Attacks? · Stable Unlearnable Example: Enhancing ...
Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation · no code implementations • 5 Mar 2023 • Yixin Liu, Chenrui Fan, Pan Zhou, Lichao Sun.
Co-authors ; Unlearnable graph: Protecting graphs from unauthorized exploitation. Y Liu, C Fan, P Zhou, L Sun. arXiv preprint arXiv:2303.02568, 2023. 4, 2023 ; 1+ ...
Unlearnable Graph: Protecting Graphs From Unauthorized Exploitation. Yixin Liu, Chenrui Fan, Pan Zhou and Lichao Sun. Read More. UnGANable: Defending Against ...
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Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation · no code implementations • 5 Mar 2023 • Yixin Liu, Chenrui Fan, Pan Zhou, Lichao Sun.
Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation. [paper] ... Graph-structured Data from Unauthorized Exploitation. [paper]; Arxiv 2023.
exploration rate that balances exploration and exploitation in -Greedy, and γ is the discounting factor that reduces the probability of exploration when the ...