Add surrogate extraction v0.2 for GNN models with class-based implementation #3
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Sparshkhare1306
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Oct 1, 2025
Refactor DFEAAttack initialization and evaluation methods
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Surrogate Extraction Fidelity Results for GNN Models
This document presents the fidelity results of surrogate models when varying the number of attack nodes for three popular datasets: Cora, Citeseer, and PubMed.
Results Summary
Dataset: Cora
Dataset: Citeseer
Dataset: PubMed
Analysis
These results suggest that the PubMed dataset is more robust to surrogate extraction attacks compared to Cora and Citeseer.
Conclusion
The fidelity of surrogate models depends on the dataset characteristics and the number of attack nodes. The results can help in understanding the robustness of Graph Neural Networks (GNNs) to surrogate extraction attacks.