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The First Workshop on Graph Learning
April 25, 2022, Online 

A workshop of The ACM Web Conference 2022: 

Graphs (also known as networks) are a 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 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, complex networks, 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
- 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 and big networks
- 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. We encourage you to submit
your paper to the Workshop on Graph Learning Benchmarks (GLB 2022@TheWebConf
2022: ) instead of this
workshop in case it contributes mainly to benchmarks of graph learning.

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 ( ), 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 ( ).

Feng Xia, Federation University Australia
Renaud Lambiotte, University of Oxford
Charu Aggarwal, IBM T. J. Watson Research Center

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