CALL FOR PAPERS
The First Workshop on Graph Learning
April 25, 2022, Online
A workshop of The ACM Web Conference 2022: https://www2022.thewebconf.org/
Graphs (also known as networks) are popular and widely-used representation of various complex data, such as World Wide Web, knowledge graphs, social networks, biological networks, traffic networks, citation networks, and communication networks. Graph data are now ubiquitous. Recent years have witnessed a surge of research and development in machine learning with/on graphs thanks to the revival of AI. This is leading to the rapid emergence of the field of graph learning. Built upon theories and techniques from multiple areas, including e.g. AI, machine learning, network science, graph theory, web science, and data science, graph learning as a powerful tool has attracted remarkable attention from many communities. Over the past few years, a lot of effective graph learning models and algorithms (e.g. graph neural networks) have been developed to address various challenges in real-world applications, with promising results achieved.
This workshop aims to bring together researchers and practitioners working on graph learning from academia and industry to discuss recent advances and core challenges of graph learning. This workshop will be established as a platform for multiple disciplines such as computer science, applied mathematics, physics, social sciences, data science, and systems engineering. Core challenges in regard to theory, methodology, and applications of graph learning will be the main center of discussions at the workshop.
In this workshop, we desire to explore the most challenging topics in the emerging field of graph learning and seek answers to noteworthy research questions such as:
- What are the core theories and models that underpin graph learning?
- How to build trustworthy and/or responsible AI systems with graph learning?
- Can graph learning be used for large-scale and complex networks/systems?
- When will graph learning fail, and why?
- How should new comers from diverse disciplines be educated so as to take advantage of graph learning?
Topics of interest include but not limited to:
- Foundations and understanding of graph learning
- Novel models and algorithms for graph learning
- Trustworthy graph learning
- Fairness, transparency, explainability, and robustness
- Graph learning on/for the Web
- Graph learning for complex systems
- Graph learning for social good
- Representation learning
- AI in knowledge graphs
- Lifelong graph learning systems
- Graph learning in various domains
- Graph learning applications, services, platforms, and education
Submission deadline: February 15, 2022 (Anywhere on Earth, Firm)
Acceptance notification: March 3, 2022
Camera-ready version: March 10, 2022
Workshop date: April 25, 2022
Authors are invited to submit original papers that must not have been submitted to or published in any other workshop, conference, or journal. The workshop will accept full papers describing completed work, work-in-progress papers with preliminary results, as well as position papers reporting inspiring and intriguing new ideas. Note that papers related to the Web are particularly welcome.
All papers should be no more than 12 pages in length (maximum 8 pages for the main paper content + maximum 2 pages for appendixes + maximum 2 pages for references). Papers must be submitted in PDF according to the ACM format published in the ACM guidelines (https://www.acm.org/publications/proceedings-template), selecting the generic “sigconf” sample. The PDF files must have all non-standard fonts embedded. Papers must be self-contained and in English.
All submissions will be peer-reviewed by members of the Program Committee and be evaluated for originality, quality and appropriateness to the workshop. At least one author of each accepted papers must present their work at the workshop. All accepted and presented papers will be published in The ACM Web Conference 2022 proceedings (companion volume), through the ACM Digital Library.
For access to the submission system, please visit the workshop website (http://www.graphlearning.net/).
Feng Xia, Federation University Australia
Renaud Lambiotte, University of Oxford
Charu Aggarwal, IBM T. J. Watson Research Center