---
title: "KDD (A*): papers and universities"
description: "KDD (ACM International Conference on Knowledge Discovery and Data Mining) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 2,961 of its papers from 2024–2026 (642 in 2024, 844 in 2025, 1,475 in 2026). By the countries of the authors' institutions, the most papers come from China (1,778), the United States (965) and Hong Kong (320)."
url: https://scholar4ai.com/proceedings/kdd
markdown_url: https://scholar4ai.com/proceedings/kdd.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "KDD (ACM International Conference on Knowledge Discovery and Data Mining) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 2,961 of its papers from 2024–2026 (642 in 2024, 844 in 2025, 1,475 in… (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)
---

# KDD: ACM International Conference on Knowledge Discovery and Data Mining — papers and institutions, 2024–2026

Papers of KDD, ACM International Conference on Knowledge Discovery and Data Mining (CORE rank A*; field: Data management and data science, Machine learning), 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

- KDD (ACM International Conference on Knowledge Discovery and Data Mining) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 2,961 of its papers from 2024–2026 (642 in 2024, 844 in 2025, 1,475 in 2026).
- By the countries of the authors' institutions, the most papers come from China (1,778), the United States (965) and Hong Kong (320).
- The institutions with the most papers are Tsinghua University (196), Zhejiang University (179) and The Hong Kong University of Science and Technology (Guangzhou) (156).
- Its most-cited paper of these years is “A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models” (2024), with 829 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 642 | 630 |
| 2025 | 844 | 841 |
| 2026 | 1,475 | 1,458 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4363608767](https://openalex.org/S4363608767) | The conference's proceedings |

## Who publishes there

*Institutions with the most KDD papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 196 |
| 2 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 179 |
| 3 | The Hong Kong University of Science and Technology (Guangzhou) | 156 |
| 4 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 135 |
| 5 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 124 |
| 6 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 113 |
| 7 | Alibaba Group (China) | 112 |
| 8 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 110 |
| 9 | Hong Kong University of Science and Technology | 106 |
| 10 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 99 |

*Countries with the most KDD papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 1,778 |
| 2 | United States | 965 |
| 3 | Hong Kong | 320 |
| 4 | Singapore | 226 |
| 5 | Australia | 146 |
| 6 | United Kingdom | 127 |
| 7 | South Korea | 121 |
| 8 | Canada | 69 |
| 9 | Germany | 63 |
| 10 | Japan | 57 |

*Keywords with the most KDD papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 480 |
| 2 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 262 |
| 3 | recommender systems | 134 |
| 4 | retrieval-augmented generation | 85 |
| 5 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 84 |
| 6 | distribution shift | 71 |
| 7 | diffusion models | 69 |
| 8 | interpretability | 62 |
| 9 | benchmark | 60 |
| 10 | LLM agents | 52 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 829 | [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://doi.org/10.1145/3637528.3671470) | 2024-08-24 | Wenqi Fan, Yujuan Ding, Liangbo Ning et al. |
| 2 | 242 | [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://doi.org/10.1145/3637528.3671451) | 2024-08-24 | Yuxuan Liang, Haomin Wen, Yuqi Nie et al. |
| 3 | 190 | [UrbanGPT: Spatio-Temporal Large Language Models](https://doi.org/10.1145/3637528.3671578) | 2024-08-24 | Zhonghang Li, Lianghao Xia, Jiabin Tang et al. |
| 4 | 142 | [FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://doi.org/10.1145/3637528.3671573) | 2024-08-24 | Weirui Kuang, Bingchen Qian, Zitao Li et al. |
| 5 | 136 | [Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System](https://doi.org/10.1145/3637528.3671931) | 2024-08-24 | Sein Kim, Hongseok Kang, Seungyoon Choi et al. |
| 6 | 134 | [OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning](https://doi.org/10.1145/3637528.3671582) | 2024-08-24 | Rui Ye, Wenhao Wang, Jingyi Chai et al. |
| 7 | 133 | [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://doi.org/10.1145/3637528.3671458) | 2024-08-24 | Sunhao Dai, Xu Chen, Shicheng Xu et al. |
| 8 | 127 | [Fredformer: Frequency Debiased Transformer for Time Series Forecasting](https://doi.org/10.1145/3637528.3671928) | 2024-08-24 | Xihao Piao, Zheng Chen, Taichi Murayama et al. |
| 9 | 122 | [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://doi.org/10.1145/3637528.3671474) | 2024-08-24 | Yashar Deldjoo, Zhankui He, Julian McAuley et al. |
| 10 | 122 | [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://doi.org/10.1145/3637528.3671662) | 2024-08-24 | Yuan Yuan, Jingtao Ding, Jie Feng et al. |

## Cite this page

Scholar4AI. (2026). KDD: ACM International Conference on Knowledge Discovery and Data Mining — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/kdd

```bibtex
@misc{scholar4ai-proceedings-kdd,
  author = {{Scholar4AI}},
  title = {KDD: ACM International Conference on Knowledge Discovery and Data Mining — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/kdd}},
  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/).