Scholar4AI Updated 2026-11-02 12:20 UTC+09:00

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. Also as Markdown.

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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)
YearPapersWith affiliations
20246262
20258887
2026200196

Sources#

Where OpenAlex lists the papers
OpenAlex sourceWhich papers
S7407086014The conference's proceedings

Who publishes there#

Institutions with the most SIGCOMM papers, 2024–2026
#InstitutionPapers
1Tsinghua University48
2Alibaba Group (China)32
3Peking University29
4Meta (United States)19
5ByteDance (China)16
6Shanghai Jiao Tong University15
7Microsoft (United States)14
8Nanjing University14
9Chinese Academy of Sciences13
10Google (United States)12
Countries with the most SIGCOMM papers, 2024–2026
#CountryPapers
1United States163
2China158
3Germany20
4United Kingdom20
5Hong Kong20
6Switzerland13
7Singapore12
8South Korea11
9France8
10Canada7
Keywords with the most SIGCOMM papers, 2024–2026
#KeywordPapers
1data center networks24
2large language models19
3congestion control15
4RDMA13
5collective communication11
6large language model training11
7Starlink10
8GPU interconnects9
9SmartNIC9
10distributed training9

Most-cited papers#

Most-cited SIGCOMM papers of 2024–2026 (citations counted by OpenAlex)
#CitationsTitlePublishedAuthors
1186Alibaba HPN: A Data Center Network for Large Language Model Training2024-07-31Kun Qian, Yongqing Xi, Jiamin Cao et al.
2180RDMA over Ethernet for Distributed Training at Meta Scale2024-07-31Adithya Gangidi, Rui Miao, Shengbao Zheng et al.
3168NetLLM: Adapting Large Language Models for Networking2024-07-31Duo Wu, Xianda Wang, Yaqi Qiao et al.
4118CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving2024-07-31Yuhan Liu, Hanchen Li, Yihua Cheng et al.
567Crux: GPU-Efficient Communication Scheduling for Deep Learning Training2024-07-31Jiamin Cao, Yu Guan, Kun Qian et al.
647Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem2024-07-31Xuting Liu, Behnaz Arzani, Siva Kesava Reddy Kakarla et al.
737FIGRET: Fine-Grained Robustness-Enhanced Traffic Engineering2024-07-31Ximeng Liu, Shizhen Zhao, Yong Cui et al.
836Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction2024-07-31Wei Ye, Xinyue Hu, Steven Sleder et al.
935SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming2024-07-31Tianyu Chen, Yiheng Lin, Nicolas Christianson et al.
1034Unveiling the 5G Mid-Band Landscape: From Network Deployment to Performance and Application QoE2024-07-31Rostand A. K. Fezeu, Claudio Fiandrino, Eman Ramadan et al.

Conferences in the same field#

WWW · INFOCOM · MobiCom · ASPLOS · ISCA · HPCA · PODC · SOSP · PerCom · SC · ICWS · ICDCS · MobiSys · IPDPS · EuroSys · IMC · ICS · ICCAD · DSN · HPDC · MMSys · ICSOC · RTAS · MSWIM · CoNEXT · RTSS · DISC · Middleware · HotOS

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

@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}
}

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