NodeArena: A Comprehensive Node Classification Benchmark for Foundation Models

Graph neural networks (GNNs) are a principal model family for graph learning but require separate training and tuning for each dataset. Graph foundation models (GFMs) aim to reduce this burden by transferring pretrained knowledge, but it remains unclear whether they offer consistent predictive gains at practical computational costs. Although node classification is the representative graph-learning task, existing benchmarks lack the combination of broad dataset coverage, controlled evaluation, and cost measurement needed to assess this promise. We introduce NodeArena, a comprehensive node classification benchmark comparing six GFMs with 15 supervised methods across 51 datasets. With the unified protocol combining shared splits, multiple metrics, and controlled hyperparameter tuning, NodeArena evaluates predictive performance as well as the computational costs of GFM adaptation, supervised training and tuning, and inference. On NodeArena, tuned GNNs remain the strongest overall, while GraphPFN reaches third in the Elo ranking under the label-rich split. GraphPFN and GVT are the two GFMs on the performance–cost frontiers, but intermediate tuning budgets only leave the former on the frontier. These findings indicate that GFMs still remain limited as broad replacements for dataset-specific training and tuning. We release the evaluation pipeline, run-level results, and a public leaderboard to support reproducible and continued comparison, available at https://anonymous.4open.science/r/node-arena-BB1B/.