---
title: "IEEE VIS (A): papers and universities"
description: "IEEE VIS (IEEE Visualization) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 450 of its papers from 2024–2026 (217 in 2024, 215 in 2025, 18 in 2026). By the countries of the authors' institutions, the most papers come from the United States (229), China (67) and Germany (43)."
url: https://scholar4ai.com/proceedings/ieee-vis
markdown_url: https://scholar4ai.com/proceedings/ieee-vis.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "IEEE VIS (IEEE Visualization) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 450 of its papers from 2024–2026 (217 in 2024, 215 in 2025, 18 in 2026). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# IEEE VIS: IEEE Visualization — papers and institutions, 2024–2026

Papers of IEEE VIS, IEEE Visualization (CORE rank A; field: Human-centred computing), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- IEEE VIS (IEEE Visualization) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 450 of its papers from 2024–2026 (217 in 2024, 215 in 2025, 18 in 2026).
- By the countries of the authors' institutions, the most papers come from the United States (229), China (67) and Germany (43).
- The institutions with the most papers are Harvard University (23), Hong Kong University of Science and Technology (19) and Institut national de recherche en sciences et technologies du numérique (18).
- Its most-cited paper of these years is “VisEval: A Benchmark for Data Visualization in the Era of Large Language Models” (2024), with 50 citations.
- OpenAlex has affiliations for only 89% of its papers, so the institution and country counts are incomplete.

## Papers per year

*IEEE VIS papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 217 | 194 |
| 2025 | 215 | 202 |
| 2026 | 18 | 6 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4393916909](https://openalex.org/S4393916909) | The conference's proceedings |
| [S84775595](https://openalex.org/S84775595) | Conference-linked journal (issue 1) |

## Who publishes there

*Institutions with the most IEEE VIS papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | Harvard University | 23 |
| 2 | Hong Kong University of Science and Technology | 19 |
| 3 | Institut national de recherche en sciences et technologies du numérique | 18 |
| 4 | University of Utah | 18 |
| 5 | Northeastern University | 17 |
| 6 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 14 |
| 7 | Georgia Institute of Technology | 14 |
| 8 | University of Washington | 13 |
| 9 | Université Paris-Saclay | 13 |
| 10 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 12 |

*Countries with the most IEEE VIS papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 229 |
| 2 | China | 67 |
| 3 | Germany | 43 |
| 4 | United Kingdom | 30 |
| 5 | France | 25 |
| 6 | Canada | 24 |
| 7 | Hong Kong | 20 |
| 8 | Austria | 19 |
| 9 | South Korea | 17 |
| 10 | Netherlands | 13 |

*Keywords with the most IEEE VIS papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | visual analytics | 52 |
| 2 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 51 |
| 3 | interactive visualization | 43 |
| 4 | data visualization | 39 |
| 5 | scientific visualization | 20 |
| 6 | dimensionality reduction | 17 |
| 7 | bar charts | 14 |
| 8 | visualization design | 14 |
| 9 | visualization literacy | 14 |
| 10 | network visualization | 13 |

## Most-cited papers

*Most-cited IEEE VIS papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 50 | [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://doi.org/10.1109/tvcg.2024.3456320) | 2024-09-10 | Nan Chen, Yuge Zhang, Jiahang Xu et al. |
| 2 | 40 | [An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks](https://doi.org/10.1109/tvcg.2024.3456155) | 2024-09-10 | Alexander Bendeck, John Stasko |
| 3 | 34 | [DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer](https://doi.org/10.1109/tvcg.2024.3456160) | 2024-09-10 | Andrew Wentzel, Serageldin Kamel, Xinhua Zhang et al. |
| 4 | 33 | [KnowNet:Guided Health Information Seeking from LLMs via Knowledge Graph Integration](https://doi.org/10.1109/tvcg.2024.3456364) | 2024-09-10 | Youfu Yan, Yu Hou, Yongkang Xiao et al. |
| 5 | 31 | [Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion](https://doi.org/10.1109/vis55277.2024.00027) | 2024-10-13 | Seongmin Lee, Benjamin Hoover, Hendrik Strobelt et al. |
| 6 | 29 | [Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education](https://doi.org/10.1109/tvcg.2024.3456145) | 2024-09-10 | Lin Gao, Jing Lu, Zekai Shao et al. |
| 7 | 25 | [Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning](https://doi.org/10.1109/tvcg.2024.3456159) | 2024-09-10 | Xingchen Zeng, Haichuan Lin, Yilin Ye et al. |
| 8 | 25 | [LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models](https://doi.org/10.1109/tvcg.2024.3456354) | 2024-09-10 | Minsuk Kahng, Ian Tenney, Mahima Pushkarna et al. |
| 9 | 25 | [When Refreshable Tactile Displays Meet Conversational Agents: Investigating Accessible Data Presentation and Analysis with Touch and Speech](https://doi.org/10.1109/tvcg.2024.3456358) | 2024-09-09 | Samuel Reinders, Matthew Butler, Ingrid Zukerman A et al. |
| 10 | 23 | [How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?](https://doi.org/10.1109/tvcg.2024.3456333) | 2024-09-12 | Leo Yu‐Ho Lo, Huamin Qu |

## Cite this page

Scholar4AI. (2026). IEEE VIS: IEEE Visualization — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/ieee-vis

```bibtex
@misc{scholar4ai-proceedings-ieee-vis,
  author = {{Scholar4AI}},
  title = {IEEE VIS: IEEE Visualization — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/ieee-vis}},
  urldate = {2026-11-02},
  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/).