Open
Conversation
Contributor
|
Nice work! As a minor modification, could you move all code related to EGSteal (the models that were previously placed under |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
📦 Pull Request Template
Thank you for your contribution! Please complete the checklist and provide relevant details below to help us review your PR effectively.
📋 Summary
This PR adds the implementation of the EGSteal attack into the PyGIP framework.
Specifically, it introduces:
A new EGSteal class under pygip/models/attack/EGSteal/EGSteal.py.
Integration with existing Dataset abstractions (supporting TU datasets such as AIDS, Mutagenicity, NCI1, and NCI109).
Training pipeline for target models (TargetModelGraphClassification) with GIN/GCN/GAT/GraphSAGE backbones.
Explanation alignment logic (CAM-based by default, extendable to GradCAM/GNNExplainer/PGExplainer).
Attack pipeline that queries the target model, generates surrogate data, and trains a surrogate model for evaluation.
This allows PyGIP users to reproduce the results of "How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment" and benchmark against other attacks already in the repo.
🧪 Related Issues
✅ Checklist
🧠 Additional Context (Optional)