[Please accept our apologies if you received multiple copies of this call]

CALL FOR PAPERS

*The First Workshop on Graph Learning*
April 25, 2022, Online
http://www.graphlearning.net/

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

IMPORTANT DATES:
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

SUBMISSION INSTRUCTIONS:
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/).

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

Contact Info:
Email: [log in to unmask]

############################

Unsubscribe:

[log in to unmask]

If you don't already have a password for the LISTSERV.ACM.ORG server, we recommend
that you create one now. A LISTSERV password is linked to your email
address and can be used to access the web interface and all the lists to
which you are subscribed on the LISTSERV.ACM.ORG server.

To create a password, visit:

https://LISTSERV.ACM.ORG/SCRIPTS/WA-ACMLPX.CGI?GETPW1

Once you have created a password, you can log in and view or change your
subscription settings at:

https://LISTSERV.ACM.ORG/SCRIPTS/WA-ACMLPX.CGI?SUBED1=MM-INTEREST