Home
Best Conferences - Computer Science
28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING

28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD)

Name: Knowledge Discovery and Data Mining
Start: 2022-08-14
End: 2022-08-18
Location: Washington DC

Location: Washington DC , United States

Submission deadline: 2/10/2022

Conference dates: 8/14/2022 - 8/18/2022

Research H-index

Official website

Ranking & Metrics

Discipline name	Position	Best Scientists	Publications	D-Index
Computer Science	13	741	1469	82

Call for Papers

KDD is the premier Data Science conference. We invite original technical research contributions in all aspects of the data science lifecycle including but not limited to: data cleaning and preparation, data transformation, mining, inference, learning, explainability, data privacy, and dissemination of results. Technical data science contributions that advance United Nations Sustainable Development Goals (SDGs) are also encouraged. This year we are also inviting paper submissions that are at the intersection of data science and society as part of the research track.

Data Cleaning and Preparation: A significant part of the data science lifecycle is spent on data cleaning and preparation. In several domains, data cleaning tasks continue to be rule-based and are often brittle, i.e., they break down in face of a constantly changing and evolving environment. Learning-based approaches for data cleaning and preparation which are generalizable and adaptive across domains are highly sought.

Data Transformation and Integration: The process of mapping data from one representation into another is at the heart of data science. The mapping can be query driven, based on a statistical task, or might involve integrating data from myriad sources. We seek original contributions that address the trade-off between the complexity of the transformation and algorithmic efficiency.

Mining, Inference, and Learning: These topics are the kernel of knowledge discovery from databases (KDD) paradigm and continue to witness massive growth. While classical aspects of supervised learning have been mainstreamed into the development cycle, new variations on unsupervised learning like self-supervision, few shot learning, prescriptive learning (reinforcement learning), transfer learning, meta learning, and representational learning are pushing the research boundary in a world where the proportion of labeled and annotated data is becoming minuscule. In each of these topics, we seek submissions that highlight the trade-off between accuracy, stability, robustness, and efficiency. Submissions that propose “new” inference tasks are strongly encouraged.

Explainability: As data science models are becoming part of daily human activity there is a need, often being expressed in law, that the models be fair, interpretable, and provide mechanisms to explain how a prediction or decision by the model was arrived at. Interpretable models will lead to their wider acceptance in society at large and increase the value of Data Science as a discipline in its own right.

Data Privacy and Ethics: Data privacy or lack thereof, continues to be the achilles heel of the whole data science enterprise. We seek technical contributions that advance the state of data science methods while guaranteeing individual privacy, respect for societal norms and ethical integrity.

Model Dissemination: Migrating a data science model from a research lab to a real-world deployment is non-trivial and potentially a continuous ongoing process. We seek research submissions that highlight and address technical and behavioral challenges during model deployment, feedback, and upgradation.

Data Science and Society: Data science has a critical role to play in addressing grand societal challenges, whether in addressing health inequities, climate change, resilience, sustainability, early childhood development, poverty, or other related areas. Success of data science in such areas is not just a function of data science alone, but it also requires careful engagement with the stakeholder, working across disciplines, and translation of the data science innovation towards achieving a societal impact. We invite papers that are at this interface, papers that demonstrate interdisciplinarity, papers that demonstrate stakeholder engagement, and papers that demonstrate a plan for realization of the data science application through translation. The innovation of these works may not be in the novelty of a data science method; rather, the innovation of these works will be at the careful exposition of societal challenge, and the role that data science and interdisciplinarity played towards addressing the societal challenge. The papers will be evaluated with this context. It is expected that papers have authors from different disciplines, and carefully situate the problem statement that is being solved, the role of data science, and societal impact evaluation.

Overview

The scientific conference ranking presented on this page offers a comprehensive and meticulously curated list of premier events in the field of Engineering and Technology. Developed by Research.com, a preeminent resource for science research with a longstanding reputation for providing trusted data on scientific contributions across all major fields since 2014, this ranking reflects a rigorous commitment to accuracy and academic excellence.

Conference positions in the ranking are established based on a proprietary bibliometric score devised by Research.com. This score is uniquely calculated using two pivotal factors: the estimated h-index of each conference and the number of leading scientists who have actively participated in the conference over the previous three years. Such a dual-criteria approach ensures a balanced and insightful assessment of a conference’s influence and prestige within the scientific community.

All Impact Score values for this ranking were systematically gathered as of 2024-11-27, providing the most current and reliable indicators of conference quality and impact. The ranking process was notably thorough, involving a careful examination of more than 2,262 conferences. These were selected following a rigorous analysis of over 26,934 scientific documents published within the last three years by a cohort of 9,385 distinguished scientists in the area of Engineering and Technology.

For further insight into the intricate methodology and evaluation procedures employed in calculating these ranking scores, please refer to our Methodology Page.

Papers citation over time

A key indicator for each conference 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.

Top authors and change over time

The top authors publishing at Knowledge Discovery and Data Mining (based on the number of publications) are:

Jiawei Han (121 papers) published 2 papers at the last edition, 6 less than at the previous edition,
Christos Faloutsos (90 papers) absent at the last edition,
Philip S. Yu (81 papers) published 4 papers at the last edition, 1 more than at the previous edition,
Hui Xiong (76 papers) published 11 papers at the last edition, 5 more than at the previous edition,
Jian Pei (56 papers) published 8 papers at the last edition, 3 more than at the previous edition.

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

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing at Knowledge Discovery and Data Mining (based on the number of publications) are:

IBM (351 papers) published 18 papers at the last edition, 3 more than at the previous edition,
Microsoft (344 papers) published 35 papers at the last edition, 9 more than at the previous edition,
Carnegie Mellon University (247 papers) published 11 papers at the last edition, 2 more than at the previous edition,
Tsinghua University (235 papers) published 30 papers at the last edition, 3 more than at the previous edition,
University of Illinois at Urbana–Champaign (188 papers) published 19 papers at the last edition, 1 less than at the previous edition.

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

Publication chance based on affiliation

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

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, 1.38% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 29.86% were posted by at least one author from the top 10 institutions publishing at the conference. Another 16.23% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.44% of all publications and 31.46% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference 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 conference 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 at a conference. The index includes the authors publishing at the last edition of a conference, 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.

Related Online Degrees & Career Pathways

Exploring related online degrees can expand your career opportunities beyond traditional computer science roles. For example, pursuing an accelerated psychology bachelor's degree allows students to quickly gain skills in understanding user behavior, an asset in human-computer interaction and UX design fields.

Cost is a significant factor when choosing an online program. Identifying the most affordable online psychology degree options can make interdisciplinary studies more accessible without sacrificing quality.

For those interested in the gaming industry, online colleges for game design provide specialized knowledge. The link to online colleges for game design highlights affordable programs that equip students with skills in coding, animation, and interactive media—key to game development careers.

As cybersecurity grows in importance, earning a degree from recognized cyber security schools online can prepare you for protecting digital systems and data. These programs offer practical training essential for roles like security analyst and ethical hacker.