# GraphNetz: Statistical Benchmarking of Graph Neural Networks with Paired Tests and Rank Aggregation

arXiv: https://arxiv.org/abs/2605.09099 · HTML: https://mlsanity.com/paper/2605.09099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2026.

## Built on by (most cited first, 1 of 1)

- [Generalized Graph Variational Autoencoders: Bounded Divergences Control Posterior Collapse](https://mlsanity.com/paper/2609.29546/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) benchmarks often report single point estimates, even when performance differences are small relative to variation across random seeds, train/test splits, and datasets. Confidence intervals, paired comparisons, multiple-comparison correction, and rank-based aggregation are standard statistical tools, but they are rarely the default output of graph-learning benchmark suites. We introduce GraphNetz, a benchmarking framework whose default output is a structured statistical report rather than a raw accuracy table. GraphNetz currently includes 63 dataset loaders, four task types, and five canonical GNN architectures, while also supporting custom datasets and models. The framework standardizes multi-seed evaluation and automatically returns per-cell confidence intervals, Holm-corrected paired tests, and Friedman-Nemenyi critical-difference diagrams across tasks. In a cross-category benchmark over ten heterogeneous tasks, apparent rank differences among four canonical node-level encoders fall within a single Nemenyi clique, indicating that none is significantly better than the others at α = 0.05. GraphNetz therefore provides researchers with a reproducible computational and statistical pipeline to benchmark new graph-learning methods against standard architectures, over different tasks and a wide set of applications, while reporting principled statistical evidence for benchmarking which accounts for seed uncertainty. This framework is set to serve the graph-learning community with a reproducible and honest model comparison ready to be added to papers.