Hadar Shavit

GraphBench just won the Best Paper Runner-Up Award at the Graph Foundation Models Workshop at ICML 2026!!! 🥳 We developed GraphBench as a response to the challenges raised in our "Position: Graph Learning Will Lose Relevance Due to Poor Benchmarks" ICML 2025 paper. It introduces many new and exciting tasks, such as algorithmic reasoning and hardware design, with the goal of pushing the community toward evaluating models on real, impactful problems rather than toy datasets that often fail to generalize to the real world. GraphBench also comes with predefined data splits and evaluation protocols, along with seamless PyTorch-Geometric integration, making it easy to get started. Check it out: graphbench.io

Remember our ICML25 "Graph Learning Will Lose Relevance Due To Poor Benchmarks"? Fear no more! GraphBench is here! 🤩 We give you: The next generation of Graph Benchmarking! Including: -New shiny high-quality datasets from diverse domains spanning seven domains, including chip design, algorithmic reasoning, and weather forecasting. -Standardized hyperparameter tuning procedures, enabling fair and principled model comparison - Strong, transparent baselines that accurately reflect algorithmic progress - Comprehensive coverage of graph learning tasks, datasets, and modern GNN architectures - Reproducibility-focused design, minimizing variance and evaluation artifacts - Forward-looking benchmark designed for next-generation graph learning research A huge collab with: @chrsmrrs, @mmbronstein, @michael_galkin, @HolgerHoo, Timo Stoll, @ChendiQian, @benfinkelshtein, Ali Parvis, Darius Weber, @ffabffrasca, @HadarShavit, @antoinesrdin, Arman Mielke, Marie Anastacio, Erik Müller,