posted by user: drfxia || 3305 views || tracked by 4 users: [display]

GL 2022 : The First Workshop on Graph Learning @The ACM Web Conference 2022

FacebookTwitterLinkedInGoogle

Link: http://www.graphlearning.net/
 
When Apr 25, 2022 - Apr 25, 2022
Where Online
Submission Deadline Feb 15, 2022
Notification Due Mar 3, 2022
Final Version Due Mar 10, 2022
Categories    artificial intelligence   machine learning   data mining   computational intelligence
 

Call For Papers

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. We encourage you to submit your paper to the Workshop on Graph Learning Benchmarks (GLB 2022@TheWebConf 2022: https://graph-learning-benchmarks.github.io/glb2022) 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 (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: graphlearningchairs@googlegroups.com

Related Resources

AAISS 2023   Special Issue on Advances in Artificial Intelligent Systems for the Scholarly Domain
AIM@EPIA 2023   Artificial Intelligence in Medicine
TNNLS-GL 2023   IEEE Transactions on Neural Networks and Learning Systems Special Issue on Graph Learning
ICDM 2023   International Conference on Data Mining
IEEE Xplore-Ei/Scopus-CCCAI 2023   2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI 2023) -EI Compendex
ICCV 2023   International Conference on Computer Vision
SEMANTiCS 2023   19th International Conference on Semantic Systems
ESANN 2023   European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
EAICI 2024   Explainable AI for Cancer Imaging
AIAPP 2023   9th International Conference on Artificial Intelligence and Applications