---
title: "ICDM (A*): papers and universities"
description: "ICDM (IEEE International Conference on Data Mining) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 310 of its papers from 2024–2026 (123 in 2024, 187 in 2025; 2026 not in OpenAlex yet). By the countries of the authors' institutions, the most papers come from China (119), the United States (96) and Australia (32)."
url: https://scholar4ai.com/proceedings/icdm
markdown_url: https://scholar4ai.com/proceedings/icdm.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ICDM (IEEE International Conference on Data Mining) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 310 of its papers from 2024–2026 (123 in 2024, 187 in 2025; 2026 not in OpenAlex yet). (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)
---

# ICDM: IEEE International Conference on Data Mining — papers and institutions, 2024–2026

Papers of ICDM, IEEE International Conference on 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

- ICDM (IEEE International Conference on Data Mining) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 310 of its papers from 2024–2026 (123 in 2024, 187 in 2025; 2026 not in OpenAlex yet).
- By the countries of the authors' institutions, the most papers come from China (119), the United States (96) and Australia (32).
- The institutions with the most papers are Chinese Academy of Sciences (11), Mohamed bin Zayed University of Artificial Intelligence (10) and Sun Yat-sen University (9).
- Its most-cited paper of these years is “Goal-Guided Generative Prompt Injection Attack on Large Language Models” (2024), with 26 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 123 | 116 |
| 2025 | 187 | 177 |
| 2026 | – | – |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4210214883](https://openalex.org/S4210214883) | The conference's proceedings |

## Who publishes there

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

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 11 |
| 2 | Mohamed bin Zayed University of Artificial Intelligence | 10 |
| 3 | [Sun Yat-sen University](https://scholar4ai.com/institutions/sun-yat-sen-university.md) | 9 |
| 4 | Korea Advanced Institute of Science and Technology | 8 |
| 5 | [Macquarie University](https://scholar4ai.com/institutions/macquarie-university.md) | 8 |
| 6 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 7 |
| 7 | University of Vienna | 7 |
| 8 | [Shanghai University](https://scholar4ai.com/institutions/shanghai-university.md) | 6 |
| 9 | Deakin University | 6 |
| 10 | [Shenzhen University](https://scholar4ai.com/institutions/shenzhen-university.md) | 6 |

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

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 119 |
| 2 | United States | 96 |
| 3 | Australia | 32 |
| 4 | South Korea | 16 |
| 5 | Hong Kong | 14 |
| 6 | United Kingdom | 13 |
| 7 | United Arab Emirates | 12 |
| 8 | Germany | 10 |
| 9 | Canada | 8 |
| 10 | Italy | 8 |

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

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 25 |
| 2 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 20 |
| 3 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 19 |
| 4 | anomaly detection | 12 |
| 5 | distribution shift | 11 |
| 6 | interpretability | 9 |
| 7 | recommender systems | 9 |
| 8 | self-supervised learning | 9 |
| 9 | attention mechanism | 8 |
| 10 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 8 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 26 | [Goal-Guided Generative Prompt Injection Attack on Large Language Models](https://doi.org/10.1109/icdm59182.2024.00119) | 2024-12-09 | Chong Zhang, Mingyu Jin, Qinkai Yu et al. |
| 2 | 19 | [AnomalyLLM: Few-Shot Anomaly Edge Detection for Dynamic Graphs Using Large Language Models](https://doi.org/10.1109/icdm59182.2024.00093) | 2024-12-09 | Shuo Liu, Di Yao, Lanting Fang et al. |
| 3 | 12 | [Generating Realistic Tabular Data with Large Language Models](https://doi.org/10.1109/icdm59182.2024.00040) | 2024-12-09 | Dang Nguyen, Sunil Gupta, Kien Do et al. |
| 4 | 10 | [DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation](https://doi.org/10.1109/icdm59182.2024.00066) | 2024-12-09 | Xiaoshan Yu, Chuan Qin, Qi Zhang et al. |
| 5 | 9 | [Detect, Investigate, Judge and Determine: A Knowledge-Guided Framework for Few-Shot Fake News Detection](https://doi.org/10.1109/icdm65498.2025.00055) | 2025-11-12 | Ye Liu, Jiajun Zhu, Xukai Liu et al. |
| 6 | 9 | [Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations](https://doi.org/10.1109/icdm59182.2024.00065) | 2024-12-09 | Runlong Yu, Chonghao Qiu, Robert Ladwig et al. |
| 7 | 8 | [Matrix Profile for Anomaly Detection on Multidimensional Time Series](https://doi.org/10.1109/icdm59182.2024.00114) | 2024-12-09 | Chin‐Chia Michael Yeh, Audrey Der, Uday Singh Saini et al. |
| 8 | 8 | [EMIT - Event-Based Masked Auto Encoding for Irregular Time Series](https://doi.org/10.1109/icdm59182.2024.00044) | 2024-12-09 | Hrishikesh Patel, Ruihong Qiu, Adam Irwin et al. |
| 9 | 7 | [OA 2 H-SP: One-Step Anchor-Adaptive Hypergraph Spectral Clustering](https://doi.org/10.1109/icdm65498.2025.00113) | 2025-11-12 | Kamal Berahmand, Razieh Sheikhpour, Farid Saberi-Movahed et al. |
| 10 | 7 | [xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection](https://doi.org/10.1109/icdm65498.2025.00032) | 2025-11-12 | Kamil Faber, Marcin Pietroń, Dominik Żurek et al. |

## Cite this page

Scholar4AI. (2026). ICDM: IEEE International Conference on Data Mining — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/icdm

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