---
title: "ICDE (A*): papers and universities"
description: "ICDE (International Conference on Data Engineering) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,271 of its papers from 2024–2026 (509 in 2024, 406 in 2025, 356 in 2026). By the countries of the authors' institutions, the most papers come from China (841), the United States (261) and Hong Kong (188)."
url: https://scholar4ai.com/proceedings/icde
markdown_url: https://scholar4ai.com/proceedings/icde.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ICDE (International Conference on Data Engineering) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,271 of its papers from 2024–2026 (509 in 2024, 406 in 2025, 356 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)
---

# ICDE: International Conference on Data Engineering — papers and institutions, 2024–2026

Papers of ICDE, International Conference on Data Engineering (CORE rank A*; field: 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

- ICDE (International Conference on Data Engineering) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,271 of its papers from 2024–2026 (509 in 2024, 406 in 2025, 356 in 2026).
- By the countries of the authors' institutions, the most papers come from China (841), the United States (261) and Hong Kong (188).
- The institutions with the most papers are Zhejiang University (94), Hong Kong University of Science and Technology (75) and Tsinghua University (72).
- Its most-cited paper of these years is “Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation” (2024), with 143 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 509 | 497 |
| 2025 | 406 | 396 |
| 2026 | 356 | 344 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4210210321](https://openalex.org/S4210210321) | The conference's proceedings |

## Who publishes there

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

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 94 |
| 2 | Hong Kong University of Science and Technology | 75 |
| 3 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 72 |
| 4 | East China Normal University | 66 |
| 5 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 60 |
| 6 | [Beijing Institute of Technology](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | 54 |
| 7 | The Hong Kong University of Science and Technology (Guangzhou) | 54 |
| 8 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 52 |
| 9 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 50 |
| 10 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | 48 |

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

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 841 |
| 2 | United States | 261 |
| 3 | Hong Kong | 188 |
| 4 | Australia | 114 |
| 5 | Singapore | 98 |
| 6 | Canada | 59 |
| 7 | Denmark | 46 |
| 8 | Germany | 39 |
| 9 | France | 27 |
| 10 | United Kingdom | 26 |

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

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 85 |
| 2 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 66 |
| 3 | query optimization | 37 |
| 4 | large-scale graphs | 34 |
| 5 | dynamic graphs | 32 |
| 6 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 29 |
| 7 | NP-hardness | 27 |
| 8 | knowledge graphs | 26 |
| 9 | retrieval-augmented generation | 26 |
| 10 | scalability | 26 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 143 | [Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation](https://doi.org/10.1109/icde60146.2024.00118) | 2024-05-13 | Bowen Zheng, Yupeng Hou, Hongyu Lu et al. |
| 2 | 69 | [A Unified Replay-Based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data](https://doi.org/10.1109/icde60146.2024.00085) | 2024-05-13 | Miao Hao, Yan Li Zhao, Chenjuan Guo et al. |
| 3 | 63 | [CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs](https://doi.org/10.1109/icde60146.2024.00323) | 2024-05-13 | Hiroyuki Ootomo, Akira Naruse, Corey Nolet et al. |
| 4 | 57 | [PURPLE: Making a Large Language Model a Better SQL Writer](https://doi.org/10.1109/icde60146.2024.00009) | 2024-05-13 | Tonghui Ren, Yuankai Fan, Zhenying He et al. |
| 5 | 52 | [Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection](https://doi.org/10.1109/icde60146.2024.00099) | 2024-05-13 | Yuchen Fang, Jiandong Xie, Yan Li Zhao et al. |
| 6 | 46 | [Local-Global History-Aware Contrastive Learning for Temporal Knowledge Graph Reasoning](https://doi.org/10.1109/icde60146.2024.00062) | 2024-05-13 | Wei Ren Chen, Huaiyu Wan, Yuting Wu et al. |
| 7 | 44 | [MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation](https://doi.org/10.1109/icde60146.2024.00164) | 2024-05-13 | Yunming Liao, Yang Xu, Hongli Xu et al. |
| 8 | 44 | [Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration](https://doi.org/10.1109/icde60146.2024.00284) | 2024-05-13 | Meihao Fan, Xiaoyue Han, Ju Fan et al. |
| 9 | 41 | [HeteFedRec: Federated Recommender Systems with Model Heterogeneity](https://doi.org/10.1109/icde60146.2024.00109) | 2024-05-13 | Wei Yuan, Liang Qu, Lizhen Cui et al. |
| 10 | 34 | [Graph Augmentation for Recommendation](https://doi.org/10.1109/icde60146.2024.00049) | 2024-05-13 | Qianru Zhang, Lianghao Xia, Xuheng Cai et al. |

## Cite this page

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

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