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. Also as Markdown.

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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	The conference's proceedings

Who publishes there#

Institutions with the most KDD papers, 2024–2026
#	Institution	Papers
1	Tsinghua University	196
2	Zhejiang University	179
3	The Hong Kong University of Science and Technology (Guangzhou)	156
4	Peking University	135
5	Chinese Academy of Sciences	124
6	University of Science and Technology of China	113
7	Alibaba Group (China)	112
8	Shanghai Jiao Tong University	110
9	Hong Kong University of Science and Technology	106
10	Nanyang Technological University	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	480
2	graph neural networks	262
3	recommender systems	134
4	retrieval-augmented generation	85
5	contrastive learning	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	2024-08-24	Wenqi Fan, Yujuan Ding, Liangbo Ning et al.
2	242	Foundation Models for Time Series Analysis: A Tutorial and Survey	2024-08-24	Yuxuan Liang, Haomin Wen, Yuqi Nie et al.
3	190	UrbanGPT: Spatio-Temporal Large Language Models	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	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	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	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	2024-08-24	Sunhao Dai, Xu Chen, Shicheng Xu et al.
8	127	Fredformer: Frequency Debiased Transformer for Time Series Forecasting	2024-08-24	Xihao Piao, Zheng Chen, Taichi Murayama et al.
9	122	A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)	2024-08-24	Yashar Deldjoo, Zhankui He, Julian McAuley et al.
10	122	UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction	2024-08-24	Yuan Yuan, Jingtao Ding, Jie Feng et al.

Conferences in the same field#

AAAI · NeurIPS · IJCAI · WWW · SIGIR · VLDB · ICDE · SIGMOD · ICDM · PODS · CIKM · ECAI · ECML PKDD · RecSys · ECIR · WSDM · ICWSM · ICDAR · MSR · SIGSPATIAL · PPSN · ISWC · SDM · CAiSE · ICDT · ER

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

@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}
}

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