Whether to Trust GNNs or LLMs? FairRouter for Few-Shot Node Classification

Text-attributed graphs (TAGs) couple textual semantics with graph topology, and recent GNN–LLM collaborative approaches exploit these complementary modalities to generate pseudo-labels for few-shot learning. However, existing approaches typically fix one model as the annotator or judge, regardless of which modality is more reliable for a given node, leading to biased pseudo-labels and underutilized complementary information. To this end, we propose FairRouter, which generates pseudo-labels through fair modality routing. Specifically, the router characterizes each node with multi-level evidence from both models and learns two signals: a trust score that decides whether the node yields a reliable pseudo-label, and a preference score that decides which modality to follow when the two disagree. % To generalize from the few labeled nodes, the router is trained on augmented views with semantic, topological, and modality-preference perturbations. To generalize from scarce labels, the router learns from augmented views, where semantic and topological perturbations are applied to simulate diverse modality trust and preference patterns. The routed pseudo-labels then supervise a multimodal residual classifier, which recovers nodes misclassified by both modalities while preserving their consensus. Experiments on five TAG benchmarks show that FairRouter achieves the best overall accuracy under all label budgets and the highest pseudo-label precision on all datasets, with gains of up to nine percentage points in the most label-scarce settings. The codebase is publicly availableThe code of our project is available at https://anonymous.4open.science/r/FairRouter-C6F1https://anonymous.4open.science/r/FairRouter-C6F1..