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GraphBench: Next-generation graph learning benchmarking

2025-12-04 · Timo Stoll, Chendi Qian, Ben Finkelshtein, Ali Parviz, Darius Weber, Fabrizio Frasca, Hadar Shavit, Antoine Siraudin, Arman Mielke, Marie Anastacio, Erik Müller, Maya Bechler-Speicher, Michael Bronstein, Mikhail Galkin, Holger Hoos, Mathias Niepert, Bryan Perozzi, Jan Tönshoff, Christopher Morris arxiv

Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often relying on narrow, task-specific datasets and inconsistent evaluation protocols, hindering reproducibility and broader progress. With the recent popularity of graph foundation models, these weaknesses have become apparent, as existing benchmarks are insufficient for thorough evaluation. To address these challenges, we introduce GraphBench, a comprehensive benchmark suite spanning diverse real-world domains and task settings, including node-level, edge-level, graph-level, and generative tasks. GraphBench provides standardized evaluation protocols, including consistent dataset splits and metrics for assessing out-of-distribution generalization across selected tasks, as well as a unified hyperparameter-tuning framework. We further evaluate GraphBench with recent message-passing neural networks and graph transformer models, establishing principled baselines for future research. See www.graphbench.io for further details.

📄 PDF Abstract BibTeX arXiv:2512.04475

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Tasks

Molecular Property Prediction Graph Learning

WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora

2026-02-02 · Pengyu Wang, Benfeng Xu, Licheng Zhang, Shaohan Wang 외 arxiv

Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence across multiple documents. However, many exis…

MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models

2026-05-15 · Weixin Liu, Congning Ni, Shelagh A. Mulvaney, Susannah L. Rose 외 arxiv

Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledge and how reliably they apply it to clinically salient structured ju…

Entity Typing

MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks

2026-02-24 · Rajan, Ishaan Gupta arxiv

Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction. Research has demonstrated that SMILES can be converted…

Molecular Property Prediction Transfer Learning Graph Regression

Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion

2026-02-11 · Pengcheng Zhou, Haochen Li, Zhiqiang Nie, JiaLe Chen 외 arxiv

Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge. However, the inherent discrete representation of text in existing frameworks often results in a loss …

Semantic Similarity

IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research

2023-02-27 · Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka, Tengfei Ma 외

Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-scale flexible datasets. Most existing pub…

Node Classification