Structural and Dynamics Transfer Need Different Tests
Abstract
Source-free graph adaptation and dual-robust offline learning protect different information mechanisms. Evaluation should vary topology changes, training dynamics, and deployment perturbations separately. Graph-specific and interaction-specific outcomes remain visible alongside aggregate performance. A common domain label or stable average cannot establish transfer across mechanisms omitted from the evaluation. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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Published
Versions
- 2026-10-15 (2)
- 2025-11-06 (1)