---
title: "ECML PKDD (A): papers and universities"
description: "ECML PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 786 of its papers from 2024–2026 (270 in 2024, 200 in 2025, 316 in 2026). By the countries of the authors' institutions, the most papers come from China (242), Germany (120) and the United States (93)."
url: https://scholar4ai.com/proceedings/ecml-pkdd
markdown_url: https://scholar4ai.com/proceedings/ecml-pkdd.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ECML PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex… (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)
---

# ECML PKDD: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008) — papers and institutions, 2024–2026

Papers of ECML PKDD, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008) (CORE rank A; field: Machine learning, Data management and data science), 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

- ECML PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 786 of its papers from 2024–2026 (270 in 2024, 200 in 2025, 316 in 2026).
- By the countries of the authors' institutions, the most papers come from China (242), Germany (120) and the United States (93).
- The institutions with the most papers are Centre National de la Recherche Scientifique (53), Chinese Academy of Sciences (21) and Institut national de recherche en sciences et technologies du numérique (20).
- Its most-cited paper of these years is “MedSyn: LLM-Based Synthetic Medical Text Generation Framework” (2024), with 32 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 270 | 266 |
| 2025 | 200 | 197 |
| 2026 | 316 | 313 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S106296714](https://openalex.org/S106296714) | Papers of the volumes titled “Machine Learning and Knowledge Discovery in Databases” (LNAI, research and applied data science tracks), found by DOI through Crossref |

## Who publishes there

*Institutions with the most ECML PKDD papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 53 |
| 2 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 21 |
| 3 | Institut national de recherche en sciences et technologies du numérique | 20 |
| 4 | East China Normal University | 18 |
| 5 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 17 |
| 6 | Ludwig-Maximilians-Universität München | 15 |
| 7 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 15 |
| 8 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 14 |
| 9 | Munich Center for Machine Learning | 14 |
| 10 | Deakin University | 12 |

*Countries with the most ECML PKDD papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 242 |
| 2 | Germany | 120 |
| 3 | United States | 93 |
| 4 | France | 84 |
| 5 | Italy | 47 |
| 6 | Australia | 40 |
| 7 | United Kingdom | 30 |
| 8 | Japan | 28 |
| 9 | Hong Kong | 26 |
| 10 | Canada | 25 |

*Keywords with the most ECML PKDD papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 78 |
| 2 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 55 |
| 3 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 34 |
| 4 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 22 |
| 5 | diffusion models | 22 |
| 6 | interpretability | 21 |
| 7 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 20 |
| 8 | [federated learning](https://scholar4ai.com/keywords/federated-learning.md) | 17 |
| 9 | [class imbalance](https://scholar4ai.com/keywords/class-imbalance.md) | 16 |
| 10 | computational efficiency | 16 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 32 | [MedSyn: LLM-Based Synthetic Medical Text Generation Framework](https://doi.org/10.1007/978-3-031-70381-2_14) | 2024-01-01 | Gleb Kumichev, Pavel Dmitrievich Blinov, Yulia Kuzkina et al. |
| 2 | 27 | [LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages](https://doi.org/10.1007/978-3-031-70381-2_25) | 2024-01-01 | Nataliia Kholodna, Sahib Julka, Mohammad Khodadadi et al. |
| 3 | 19 | [Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience](https://doi.org/10.1007/978-3-031-70362-1_4) | 2024-01-01 | Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen et al. |
| 4 | 16 | [Advancing Graph Counterfactual Fairness Through Fair Representation Learning](https://doi.org/10.1007/978-3-031-70368-3_3) | 2024-01-01 | Zichong Wang, Zhibo Chu, Ronald Blanco et al. |
| 5 | 15 | [Simple Graph Condensation](https://doi.org/10.1007/978-3-031-70344-7_4) | 2024-01-01 | Zhenbang Xiao, Yu Wang, Shunyu Liu et al. |
| 6 | 14 | [Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection](https://doi.org/10.1007/978-3-031-70365-2_9) | 2024-01-01 | Yutong Chen, Hongzuo Xu, Guansong Pang et al. |
| 7 | 12 | [Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models](https://doi.org/10.1007/978-3-031-70368-3_21) | 2024-01-01 | Gustavo Escobedo, Marta Moscati, Peter Müllner et al. |
| 8 | 12 | [Individual Fairness with Group Awareness Under Uncertainty](https://doi.org/10.1007/978-3-031-70362-1_6) | 2024-01-01 | Zichong Wang, Jocelyn Dzuong, Xiaoyong Yuan et al. |
| 9 | 10 | [HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction](https://doi.org/10.1007/978-3-031-70365-2_21) | 2024-01-01 | Bin Liu, Siqi Wu, Jin Wang et al. |
| 10 | 10 | [PSP: Pre-training and Structure Prompt Tuning for Graph Neural Networks](https://doi.org/10.1007/978-3-031-70362-1_25) | 2024-01-01 | Qingqing Ge, Zeyuan Zhao, Yiding Liu et al. |

## Cite this page

Scholar4AI. (2026). ECML PKDD: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008) — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/ecml-pkdd

```bibtex
@misc{scholar4ai-proceedings-ecml-pkdd,
  author = {{Scholar4AI}},
  title = {ECML PKDD: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008) — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/ecml-pkdd}},
  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/).