---
title: "SIGCOMM (A*): papers and universities"
description: "SIGCOMM (ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 350 of its papers from 2024–2026 (62 in 2024, 88 in 2025, 200 in 2026). By the countries of the authors' institutions, the most papers come from the United States (163), China (158) and Germany (20)."
url: https://scholar4ai.com/proceedings/sigcomm
markdown_url: https://scholar4ai.com/proceedings/sigcomm.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "SIGCOMM (ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 350 of its papers from 2024–2026… (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)
---

# SIGCOMM: ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication — papers and institutions, 2024–2026

Papers of SIGCOMM, ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (CORE rank A*; field: Distributed computing and systems software), 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

- SIGCOMM (ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 350 of its papers from 2024–2026 (62 in 2024, 88 in 2025, 200 in 2026).
- By the countries of the authors' institutions, the most papers come from the United States (163), China (158) and Germany (20).
- The institutions with the most papers are Tsinghua University (48), Alibaba Group (China) (32) and Peking University (29).
- Its most-cited paper of these years is “Alibaba HPN: A Data Center Network for Large Language Model Training” (2024), with 186 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 62 | 62 |
| 2025 | 88 | 87 |
| 2026 | 200 | 196 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S7407086014](https://openalex.org/S7407086014) | The conference's proceedings |

## Who publishes there

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

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 48 |
| 2 | Alibaba Group (China) | 32 |
| 3 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 29 |
| 4 | Meta (United States) | 19 |
| 5 | ByteDance (China) | 16 |
| 6 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 15 |
| 7 | Microsoft (United States) | 14 |
| 8 | [Nanjing University](https://scholar4ai.com/institutions/nanjing-university.md) | 14 |
| 9 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 13 |
| 10 | Google (United States) | 12 |

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

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 163 |
| 2 | China | 158 |
| 3 | Germany | 20 |
| 4 | United Kingdom | 20 |
| 5 | Hong Kong | 20 |
| 6 | Switzerland | 13 |
| 7 | Singapore | 12 |
| 8 | South Korea | 11 |
| 9 | France | 8 |
| 10 | Canada | 7 |

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

| # | Keyword | Papers |
|---:|---|---:|
| 1 | data center networks | 24 |
| 2 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 19 |
| 3 | congestion control | 15 |
| 4 | RDMA | 13 |
| 5 | collective communication | 11 |
| 6 | large language model training | 11 |
| 7 | Starlink | 10 |
| 8 | GPU interconnects | 9 |
| 9 | SmartNIC | 9 |
| 10 | distributed training | 9 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 186 | [Alibaba HPN: A Data Center Network for Large Language Model Training](https://doi.org/10.1145/3651890.3672265) | 2024-07-31 | Kun Qian, Yongqing Xi, Jiamin Cao et al. |
| 2 | 180 | [RDMA over Ethernet for Distributed Training at Meta Scale](https://doi.org/10.1145/3651890.3672233) | 2024-07-31 | Adithya Gangidi, Rui Miao, Shengbao Zheng et al. |
| 3 | 168 | [NetLLM: Adapting Large Language Models for Networking](https://doi.org/10.1145/3651890.3672268) | 2024-07-31 | Duo Wu, Xianda Wang, Yaqi Qiao et al. |
| 4 | 118 | [CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving](https://doi.org/10.1145/3651890.3672274) | 2024-07-31 | Yuhan Liu, Hanchen Li, Yihua Cheng et al. |
| 5 | 67 | [Crux: GPU-Efficient Communication Scheduling for Deep Learning Training](https://doi.org/10.1145/3651890.3672239) | 2024-07-31 | Jiamin Cao, Yu Guan, Kun Qian et al. |
| 6 | 47 | [Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem](https://doi.org/10.1145/3651890.3672249) | 2024-07-31 | Xuting Liu, Behnaz Arzani, Siva Kesava Reddy Kakarla et al. |
| 7 | 37 | [FIGRET: Fine-Grained Robustness-Enhanced Traffic Engineering](https://doi.org/10.1145/3651890.3672258) | 2024-07-31 | Ximeng Liu, Shizhen Zhao, Yong Cui et al. |
| 8 | 36 | [Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction](https://doi.org/10.1145/3651890.3672250) | 2024-07-31 | Wei Ye, Xinyue Hu, Steven Sleder et al. |
| 9 | 35 | [SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming](https://doi.org/10.1145/3651890.3672260) | 2024-07-31 | Tianyu Chen, Yiheng Lin, Nicolas Christianson et al. |
| 10 | 34 | [Unveiling the 5G Mid-Band Landscape: From Network Deployment to Performance and Application QoE](https://doi.org/10.1145/3651890.3672269) | 2024-07-31 | Rostand A. K. Fezeu, Claudio Fiandrino, Eman Ramadan et al. |

## Cite this page

Scholar4AI. (2026). SIGCOMM: ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/sigcomm

```bibtex
@misc{scholar4ai-proceedings-sigcomm,
  author = {{Scholar4AI}},
  title = {SIGCOMM: ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/sigcomm}},
  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/).