Artificial Intelligence for Science of Science (AI4SciSci)

Co-located with IEEE ICDM 2023.

Shanghai, China - December 1-4 2023

Workshop date: Dec 1st 2023


From Newton's laws to quantum mechanics, what are the key factors driving the evolution of science? Science of Science (SciSci) is an emerging research field that bears the mission to answer this question with scholarly data -- the digital trace of scientific endeavor and influxes. Recent years have witnessed many exciting advances, which have been documented in papers, patents, research grants and awards, paper citations, scientist collaboration and mobility. These data are becoming increasingly accessible in digital platforms, lending unprecedented opportunities to establish mathematical models to quantify our understanding of scientific evolution. Yet, scholarly big data has imposed new challenges for traditional SciSci research, in the sense that they are:

1) heterogeneous and multi-modal (e.g., texts, figures, tables, equations),
2) with complex topological structures (e.g., scholarly knowledge networks),
3) continuously generated at a rapid pace.

Leveraging scholarly big data to answer key SciSci questions will require methodologies beyond traditional statistical modeling.

Data Mining (DM) and artificial intelligence (AI), which have shown great potential to uncover novel knowledge from such massive data, are thus expected to take a crucial role at the frontier of SciSci research. Here we propose the AI4SciSci workshop, focused on empirical findings, methodological papers, theoretical underpinnings, and conceptual insights related to DM and AI in the broad research field of SciSci. By connecting the research communities of DM, AI, and science of science, this interdisciplinary event will not only introduce new data-centric tools to answer questions in the SciSci research, but also inspire the development of policy-relevant prediction tasks to push the frontiers of data mining research.

Call for Papers

This workshop is aimed at bringing together researchers from the areas of data mining (DM), artificial intelligence (AI), and Science of Science (SciSci). We expect to encourage an exchange of ideas and perceptions through the workshop, focusing on novel research directions, challenges, and techniques in the intersectional areas of AI and DM for SciSci. We welcome papers on topics of interest that include, but are not limited to:

1. Scholarly Graph Analytics
  • Citation networks and their applications for SciSci
  • Social networks and their applications for SciSci
  • Scientific knowledge graph construction and applications for SciSci
  • Scientometrics
2. Data Mining, Learning, and Prediction Tasks for SciSci
  • Innovation/Novelty detection & prediction
  • Scholar collaboration & mobility prediction for SciSci
  • Scientific career trajectory prediction, analysis, and uncertainties
  • Numerical modeling on science development
  • Causal inference/reasoning for SciSci
  • Streaming scientific data mining and applications for SciSci
3. Scientific Document Processing & Understanding
  • Scientific document parsing and applications for SciSci
  • Scientific metadata extraction and applications for SciSci
  • Scientific claim extraction and verification
  • Scientific table/spreadsheet structure recognition and understanding
  • Scientific figure/image extraction and understanding
  • Scientific document summarization and topic modeling
4. SciSci for Data Mining and AI
  • Scientific reproducibility, replicability, repeatability, and generalizability
  • Innovation automation with AI
  • Scientific goods for AI and ethical implications
  • Socio-economic effect on science progress and innovation
  • Scientific misinformation and disinformation
  • Scientific impact quantification using metrics outside of academia (e.g., patents, technology transfer to industry and revenue)

Submission and Publication

Paper submission link: International Workshop on Artificial Intelligence for Science of Science (AI4SciSci) .

Paper submissions should be limited to a maximum of 8 pages, and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2023 Submission Guidelines.

All accepted papers will be included in the ICDM'23 Workshop Proceedings (ICDMW 2023) published by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

All accepted papers, including workshops, must have at least one “FULL” registration. A full registration is either a “member” or “non-member” registration. Student registrations are not considered full registrations. All authors are required to register by 15th October 2023.

For registration queries please contact:

Important Dates

  • Paper Submission: September 15th, 2023
  • Author Notification: September 30th, 2023
  • Camera-Ready: October 15th, 2023
  • Registration: October 15th, 2023
  • Conference Date: December 1st - 4th, 2023
  • Workshop date: Dec 1st 2023


Time (Beijing Time)




Opening Remarks



Keynote 1

Dr. C. Lee Giles


Citation Style Classification: a Comparison of Machine Learning Approaches

Artyom Kopan, Anna Smirnova, Ilya Shchuckin, Vladislav Makeev, and George Chernishev


Can machine learning algorithms predict publication outcomes? A case study of COVID-19 preprints

Sai Koneru, Xin Wei, Jian Wu, and Sarah Rajtmajer


Hard Anomaly Detection: An Adversarial Data Augmentation Solution

Teng Hu, Cheng Wang, Qing Yang, and Xue Chen


Towards an Artificial Muse for new Ideas in Science

Dr. Mario Krenn


Ending Remarks


Keynote speaker

Professor, Max Planck Institute, Germany
  • Research group leader of the Artificial Scientist Lab at the Max Planck Institute for the Science of Light;
  • Mario has been working on interesting topics on scientific understanding with artificial intelligence, published in Nature.
  • Mario is excited about the potential of artificial intelligence-inspired and -augmented science, and how we can use algorithms in a more “creative” way

Program Committee

Professor, University Post & Telecommunication, Jiangsu, China

Webpage : Song Deng

Professor, Pennsylvania State University, Pennsylvania, USA

Webpage : C. Lee Giles

Assistant Professor, University of Notre Dame, Indiana, USA

Webpage : Meng Jiang

Professor, Griffith University, Queensland, Australia

Webpage : Shirui Pan

Assistant Professor, Pennsylvania State University, Pennsylvania, USA

Webpage : Sarah Rajtmajer

Associate Professor, Southwest University, Chongqing, China

Webpage : Di Wu

Professor, RMIT University, Australia

Webpage : Feng Xia

Assistant Professor, Cornell University, New York, USA

Webpage : Yian Yin

Associate Professor, Yanshan University, Hebei, China

Webpage : Dianlong You

Professor, Florida Atlantic University, Florida, USA

Webpage : Xingquan Zhu

Assistant Professor, Stanford University, California, USA

Webpage : James Zou



                    Jian Wu, Ph.D.
                    Assistant Professor
                    Department of Computer Science
                    Old Dominion University
                    3202 ECS Building, Norfolk, VA, 23529
                    Tel: 757-683-7753
                    Webpage: Homepage

                    Yi He, Ph.D.
                    Assistant Professor
                    Department of Computer Science
                    Old Dominion University
                    3108 ECS Building, Norfolk, VA 23529
                    Tel: 757-683-7821
                    Webpage: Homepage