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ACM Computing Surveys (ACS CS)

Publisher :

Association for Computing Machinery

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Mathematics
  • Computing Machinery
  • Theoretical Computer Science

    • +2

e-ISSN :

1557-7341

Issue Frequency :

Quarterly

Impact Factor :

16.6

p-ISSN :

0360-0300

Est. Year :

1969

Mobile :

2128697440

DOI :

YES

Country :

United States

Language :

English

APC :

YES

Impact Factor Assignee :

SJR

Email :

csur-admin@acm.org

Journal Descriptions

ACM is pleased to announce that its Publications Board has approved an updated Peer Review Policy. If you have any questions regarding the update, the associated FAQ addresses topics such as confidentiality, the use of large language models in the peer review process, conflicts of interest, and several other relevant concerns. If there are any issues that are not addressed in the FAQ, please contact ACM’s Director of Publications, Scott Delman.


ACM Computing Surveys (ACS CS) is :

International, Peer-Reviewed, Open Access, Refereed, Mathematics, Computing Machinery, Theoretical Computer Science, Computer Science, General Computer Science , Online or Print, Quarterly Journal

UGC Approved, ISSN Approved: P-ISSN - 0360-0300, E-ISSN - 1557-7341, Established in - 1969, Impact Factor - 16.6

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Indexed in Scopus

Not indexed in WoS, DOAJ, PubMed, UGC CARE

Indexing

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SJR
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WorldCat

+3 More

Publications of ACS CS

  • dott image August, 2020

Machine Learning Methods for Data Association in Multi-Object Tracking

Data association is a key step within the multi-object tracking pipeline that is notoriously challenging due to its combinatorial nature. A popular and general way to formulate data associat...

  • dott image October, 2024

State of the Art and Potentialities of Graph-level Learning

Graphs have a superior ability to represent relational data, such as chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has...

  • dott image August, 2024

Explaining the Explainers in Graph Neural Networks: a Comparative Study

Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need f...

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