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ACM Computing Surveys
H-index 109

ACM Computing Surveys

0360-0300

Published by: ACM

https://dl.acm.org/journal/csur

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 7 675 644 108

Additional Metrics

Number of Best Scientists*: 727
Documents by Best Scientists*: 666
Top 100 Ranked Scientists*: 26
SCIMAGO H-index: 232
SCIMAGO SJR: 5.797
Impact Factor: 28

Overview

Top Research Topics at ACM Computing Surveys?

The topics of Artificial intelligence, Data science, Programming language, Field (computer science) and Computer security are the focal point of discussions in the journal. It explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning, Computer vision and Natural language processing. The research on Data science discussed in the journal draws on the closely related field of Taxonomy (general).

The works on Programming language deal in particular with Programming paradigm.

  • Artificial intelligence (13.75%)
  • Data science (12.01%)
  • Programming language (8.97%)

What are the most cited papers published in the journal?

  • Data clustering: a review (11615 citations)
  • Machine learning in automated text categorization (6790 citations)
  • Anomaly detection: A survey (6603 citations)

Research areas of the most cited articles at ACM Computing Surveys:

The journal articles investigate areas of study like Artificial intelligence, Data mining, Information retrieval, Distributed computing and Programming language. While Artificial intelligence is the key highlight in the most cited papers, thet also covered some subjects on Field (computer science) and Data science. The published papers dive deep in exploring the relationship between the study of Information retrieval and World Wide Web.

What topics the last edition of the journal is best known for?

  • Artificial intelligence
  • Operating system
  • Programming language

The previous edition focused in particular on these issues:

The concepts of Artificial intelligence, Data science, Field (computer science), Deep learning and Machine learning are tackled in the journal. Topics in Artificial intelligence were tackled in line with various other fields like Structure (mathematical logic), Task (project management) and Natural language processing. In the journal, Domain (software engineering), Context (language use), Categorization, Taxonomy (general) and Big data are investigated in conjunction with one another to address concerns in Data science research.

ACM Computing Surveys links adjacent topics like Taxonomy (general) with Computer security. The research on Deep learning featured in the journal combines topics in other fields like Key (cryptography) and Convolutional neural network.

The most cited articles from the last journal are:

  • A Survey on Bias and Fairness in Machine Learning (112 citations)
  • Deep Learning for Anomaly Detection: A Review (105 citations)
  • Deep Learning--based Text Classification: A Comprehensive Review (88 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in ACM Computing Surveys (based on the number of publications) are:

  • Rajkumar Buyya (21 papers) published 1 paper at the last edition, 2 less than at the previous edition,
  • Azzedine Boukerche (19 papers) published 4 papers at the last edition, 2 less than at the previous edition,
  • Peter J. Denning (12 papers) published 1 paper at the last edition,
  • S. Sitharama Iyengar (10 papers) published 1 paper at the last edition,
  • Albert Y. Zomaya (8 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in ACM Computing Surveys (based on the number of publications) are:

  • IBM (57 papers) published 1 paper at the last edition the same number as at the previous edition,
  • Carnegie Mellon University (43 papers) published 2 papers at the last edition,
  • University of Melbourne (34 papers) published 2 papers at the last edition, 2 less than at the previous edition,
  • University of Maryland, College Park (31 papers) published 1 paper at the last edition,
  • University of Illinois at Urbana–Champaign (31 papers) published 1 paper at the last edition the same number as at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 11.44% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 6.74% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.87% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.04% of all publications and 71.35% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

How to Contribute to ACM Computing Surveys

ACM Computing Surveys provides a platform for researchers, academics, and students to explore and contribute to the latest findings in various realms of computing. Converting your research into a substantial contribution to this high-traffic, widely respected journal can add significant value to your academic career. Your research could be the next most-cited paper, therefore it's essential to understand the submission process and adhere to the journal's guidelines to ensure your work is considered for publication. To start, you'll need to prepare your research paper following the ACM guide for authors. Your paper should exhibit a deep understanding of the subject matter, include credible references, and adhere to the high standards of academic writing. Topics that are currently dominating discussions in the journal include artificial intelligence, data science, programming language, field (computer science), and computer security. Besides, if you're keen to delve into a career as a scholar, teaching position, for instance, becoming a history teacher may offer a fulfilling career path. If Texas region suits you, the steps to align yourself with the history teacher requirements in Texas could be your next move. In summary, engaging in well-structured, high impact research not only allows you to make significant contributions to your field of study but also greately benefits your career. So start developing your research strategies, align with respected journals like ACM Computing Surveys, and contribute to global knowledge today!

Top Publications

  • Deep Learning for Anomaly Detection: A Review

    Guansong Pang;Chunhua Shen;Longbing Cao;Anton Van Den Hengel

    (2021)
    3741 Citations
  • A Survey on Bias and Fairness in Machine Learning

    Ninareh Mehrabi;Fred Morstatter;Nripsuta Saxena;Kristina Lerman

    (2021)
    3674 Citations
  • Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

    (2021)
    3313 Citations
  • Generalizing from a Few Examples: A Survey on Few-shot Learning

    Yaqing Wang;Quanming Yao;James T. Kwok;Lionel M. Ni

    (2020)
    3124 Citations
  • Transformers in Vision: A Survey

    (2021)
    2912 Citations
  • Survey of Hallucination in Natural Language Generation

    Unknown

    (2022)
    2679 Citations
  • Deep Learning--based Text Classification: A Comprehensive Review

    Shervin Minaee;Nal Kalchbrenner;Erik Cambria;Narjes Nikzad

    (2021)
    1665 Citations
  • A Survey of Deep Active Learning

    Unknown

    (2020)
    1547 Citations
  • Knowledge Graphs

    Aidan Hogan;Eva Blomqvist;Michael Cochez;Claudia d'Amato

    (2020)
    1261 Citations
  • Diffusion Models: A Comprehensive Survey of Methods and Applications

    (2022)
    989 Citations

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