---
title: "Open Graph Benchmark: tasks and access"
description: "Open Graph Benchmark: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/open-graph-benchmark
markdown_url: https://scholar4ai.com/datasets/open-graph-benchmark.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Open Graph Benchmark: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Open Graph Benchmark

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/5.md)

Citation, molecule and other graphs used to compare graph neural networks.

- data category: Graph learning benchmarks
- 0 papers in the last 7 days
- Names searched for: Open Graph Benchmark, ogbn-arxiv, ogbn-products, ogbg-molhiv

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Graph learning benchmarks datasets: https://scholar4ai.com/datasets/graph-learning-benchmarks.md

## Tasks, access and usage conditions

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

*Dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| OGB — task-specific releases | Node, link and whole-graph property prediction across distinct datasets. Evaluation: Use the task’s official metric and split (which may be temporal or scaffold-based); an OGB score is not one universal metric. | [Official source / documentation](https://ogb.stanford.edu/docs/dataset_overview/) Select a named dataset (for example ogbn-arxiv or ogbg-molhiv), use the supplied split and evaluator, and record the package/data version. | Check the license on each OGB dataset page; a single package license does not apply to every source graph. |

Guide updated 2026-11-05.

## Key findings

- From Nov 2023 to Oct 2026, 86 archived papers mentioned Open Graph Benchmark in their title, abstract, keywords or full text, most of them in Aug 2025 (7).
- No paper of the tracked journals mentioned Open Graph Benchmark in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), Open Graph Benchmark was mentioned in 92 papers in the tracked journals, most of them in AI (91) and information systems (1).
- 28 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Beijing University of Posts and Telecommunications (6), National University of Singapore (5) and Jingdong (China) (4).
- The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (18), IEEE Transactions on Knowledge and Data Engineering (17) and IEEE Transactions on Neural Networks and Learning Systems (10).
- The most-cited of them is “Large Language Models on Graphs: A Comprehensive Survey” (IEEE Transactions on Knowledge and Data Engineering, 2024), with 155 citations.
- Papers use Open Graph Benchmark most often with Cora / CiteSeer / PubMed (Planetoid) (25), Molecule benchmarks (QM9, ZINC, MUTAG) (25) and Reddit (21); it has 18 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts, keywords and full texts)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 25 | 29 | 32 | 86 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 29 | 28 | 27 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| All categories | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 29 | 28 | 28 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 18 |
| 2 | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 17 |
| 3 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 10 |
| 4 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 9 |
| 5 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 8 |
| 6 | [Information Sciences](https://scholar4ai.com/journals/information-sciences.md) | 7 |
| 7 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 4 |
| 8 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 3 |
| 9 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 3 |
| 10 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Beijing University of Posts and Telecommunications](https://scholar4ai.com/institutions/beijing-university-of-posts-and-telecommunications.md) | 6 |
| 2 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 5 |
| 3 | [Jingdong (China)](https://scholar4ai.com/companies/jingdong-china.md) | 4 |
| 4 | [The University of Queensland](https://scholar4ai.com/institutions/the-university-of-queensland.md) | 4 |
| 5 | Athena Research and Innovation Center In Information Communication & Knowledge Technologies | 3 |
| 6 | [China Telecom](https://scholar4ai.com/companies/china-telecom.md) | 3 |
| 7 | [Griffith University](https://scholar4ai.com/institutions/griffith-university.md) | 3 |
| 8 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | 3 |
| 9 | [Macquarie University](https://scholar4ai.com/institutions/macquarie-university.md) | 3 |
| 10 | National Centre of Scientific Research "Demokritos" | 3 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 49 |
| 2 | United States | 18 |
| 3 | Australia | 14 |
| 4 | Singapore | 7 |
| 5 | United Kingdom | 6 |
| 6 | Hong Kong | 6 |
| 7 | Switzerland | 4 |
| 8 | India | 4 |
| 9 | Germany | 3 |
| 10 | Greece | 3 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 55 |
| 2 | graph representation learning | 15 |
| 3 | node classification | 15 |
| 4 | large-scale graphs | 11 |
| 5 | over-smoothing | 11 |
| 6 | [graph convolutional networks](https://scholar4ai.com/keywords/graph-convolutional-networks.md) | 8 |
| 7 | graph condensation | 6 |
| 8 | graph transformer | 6 |
| 9 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 6 |
| 10 | link prediction | 6 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 155 | 6.1 | [Large Language Models on Graphs: A Comprehensive Survey](https://doi.org/10.1109/tkde.2024.3469578) | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 2024-09-27 | Bowen Jin, Gang Liu, Chi Han et al. |
| 2 | 103 | 2.8 | [Generalizing Graph Neural Networks on Out-of-Distribution Graphs](https://doi.org/10.1109/tpami.2023.3321097) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2023-10-02 | Shaohua Fan, Xiao Wang, Chuan Shi et al. |
| 3 | 78 | 3.2 | [CDC: A Simple Framework for Complex Data Clustering](https://doi.org/10.1109/tnnls.2024.3473618) | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 2024-10-14 | Zhao Kang, Xuanting Xie, Bingheng Li et al. |
| 4 | 62 | 1.9 | [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://doi.org/10.1109/tpami.2023.3303431) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-02-22 | Maciej Besta, Torsten Hoefler |
| 5 | 61 | 2.6 | [Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy](https://doi.org/10.1109/tnnls.2024.3486109) | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 2024-11-12 | Thanveer Shaik, Xiaohui Tao, Haoran Xie et al. |

## Used together with

Papers use Open Graph Benchmark most often with Cora / CiteSeer / PubMed (Planetoid) (25), Molecule benchmarks (QM9, ZINC, MUTAG) (25) and Reddit (21); it has 18 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use Open Graph Benchmark together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Cora / CiteSeer / PubMed (Planetoid)](https://scholar4ai.com/datasets/cora-citeseer-pubmed-planetoid.md) | Dataset | 25 | 115.0 |
| [Molecule benchmarks (QM9, ZINC, MUTAG)](https://scholar4ai.com/datasets/molecule-benchmarks-qm9-zinc-mutag.md) | Dataset | 25 | 120.4 |
| [Reddit](https://scholar4ai.com/datasets/reddit.md) | Dataset | 21 | 18.1 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 14 | 7.2 |
| [IMDb](https://scholar4ai.com/datasets/imdb.md) | Dataset | 12 | 26.6 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | Dataset | 8 | 1.4 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 8 | 1.8 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 7 | 2.6 |
| [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | Dataset | 6 | 2.8 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 5 | 0.6 |
| [Yelp](https://scholar4ai.com/datasets/yelp.md) | Dataset | 5 | 7.1 |
| [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md) | Dataset | 5 | 1.4 |

The whole network: https://scholar4ai.com/code/network.md

## Cite this page

Scholar4AI. (2026). Open Graph Benchmark. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/open-graph-benchmark

```bibtex
@misc{scholar4ai-datasets-open-graph-benchmark,
  author = {{Scholar4AI}},
  title = {Open Graph Benchmark},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/open-graph-benchmark}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).