Feat/implementation iqra

[Iqra171]

📦 Pull Request: Add Implementations and Example Scripts for Tables 3–8 (Zhou et al. 2024)

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This PR adds the full experimental implementations and lightweight example scripts reproducing Tables 3–8 from Zhou et al. (2024), “Revisiting Black-box Ownership Verification for Graph Neural Networks,” following the PyGIP project conventions.

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📋 Summary

This PR introduces complete experiment pipelines and aligned example scripts, structured according to PyGIP’s dataset, model, and device conventions.

Changes introduced:

• Implemented all core experiments under the implementation/ folder:
  • run_bboxve.py — Table 3 (BBoxVe)
  • run_bgrove.py — Table 4 (BGrOVe)
  • run_table5_full.py — Table 5 & Figure 3
  • adversial.py — Tables 6 & 7
  • adversial_table8.py — Table 8
• Added reviewer-friendly example scripts under examples/, one for each table, to demonstrate how to invoke the corresponding implementation.
• Organized experiment results under results/ (CSV outputs per table).

Structural improvements:

• Updated dataset wrappers in pygip/datasets/ for compatibility with the experiment structure.
• Refactored model backbones under pygip/models/nn/pyg_backbones.py to ensure consistent import paths (e.g., from pygip.models.nn.pyg_backbones import GCN, GAT, GraphSAGE, ...).
• These changes only improve consistency and modularity; they do not modify the experimental logic or results.

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✅ Checklist

• My code follows the project's coding style
• I have tested the changes and verified that they work
• I have added necessary documentation (example scripts + folder structure)
• I have linked related issues above (if any)
• The PR is made from a feature branch (feat/implementation_iqra)

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🧠 Additional Context (Optional)

• All experiments replicate reported behavior from Zhou et al. (2024).
• Example scripts provide simple, reproducible entry points for reviewers. In case of an error, you can just run the script directly.
• Dataset and backbone structure now align fully with PyGIP’s import hierarchy.

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