GapTune: Learning Graph Prompts from Source–Target Context Gaps

Graph prompting adapts *graph neural networks* (GNNs) pretrained on source graphs to target applications with few trainable parameters. Recent methods introduce feature, topology, and message prompts, but how source–target context gaps should inform local prompt values remains underexplored. In a controlled prompt-transfer study with a frozen GNN, we find that prompt transferability is associated with source–target context shifts in node representations and messages. Motivated by this association, we propose GapTune, a graph prompting framework that learns local prompts from context gaps in source-available and source-free settings. Source observations are obtained from pretraining graphs when available or approximated by responses to compact model-inverted proxy graphs. Shared learnable queries pool the frozen GNN's unprompted initial node states and layer-wise messages into paired source and target contexts, whose differences supply prompt values. Local relevance weights and shared signed gates combine these values into task-supervised local prompts. Our analysis establishes gap-dependent prompt bounds, task-compatible symmetries, and conditional prediction stability under source-context approximation for a fixed adapted predictor. Experiments against 12 prompting baselines on 9 target datasets and 4 tasks show task-dependent transfer gains. Joint gap-valued prompts outperform equally parameterized free values on four ablation targets, with benefits depending on insertion locations and graph shifts.