Conference Information
GMLR 2023: Graph Models for Learning and Recognition
Submission Date:
2022-10-31 Extended
Notification Date:
Conference Date:
Tallinn, Estonia
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Call For Papers
Submission deadline EXTENDED to: October 31, 2022

			Call for Papers

Graph Models for Learning and Recognition (GMLR) Track
The 38th ACM Symposium on Applied Computing (SAC 2023)
	March 27 - April 2, 2023, Tallinn, Estonia

Track Chairs
Donatello Conte (University of Tours)
Alessandro D'Amelio (University of Milan)
Giuliano Grossi (University of Milan)
Raffaella Lanzarotti (University of Milan)
Jianyi Lin (Università Cattolica del Sacro Cuore)

Scientific Program Committee
Annalisa Barla (University of Genoa)
Davide Boscaini (Bruno Kessler Foundation)
Antonella Carbonaro (University of Bologna)
Vittorio Cuculo (University of Milan)
Samuel Feng (Sorbonne University Abu Dhabi)
Gabriele Gianini (University of Milan)
Andreas Henschel (Khalifa University)
Francesco Isgrò (University of Naples)
Giosuè Lo Bosco (University of Palermo)
Alessio Micheli (University of Pisa)
Carlos Oliver (ETH Zürich)
Maurice Pagnucco (University of New South Wales)
Jean-Yves Ramel (University of Tours)
Ryan A. Rossi (Adobe Research)
(others to be confirmed)

Important Dates
Submission of regular papers:	extended to October 31, 2022
Notification of acceptance/rejection: 	December 5, 2022
Camera-ready copies of accepted papers: December 13, 2022
SAC Conference:	March 27 - April 2, 2023

Motivations and topics
The ACM Symposium on Applied Computing (SAC 2023) has been a primary gathering 
forum for applied computer scientists, computer engineers, software engineers, 
and application developers from around the world. SAC 2023 is sponsored by the 
ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in 
Tallinn, Estonia. The technical track on Graph Models for Learning and 
Recognition (GMLR) is the second edition and is organized within SAC 2023.

This track intends to focus on all aspects of graph-based representations and 
models for learning and recognition tasks. GMLR spans, but is not limited to, 
the following topics:
● Graph Neural Networks: theory and applications
● Deep learning on graphs
● Graph or knowledge representational learning
● Graphs in pattern recognition
● Graph databases and linked data in AI
● Benchmarks for GNN
● Dynamic, spatial and temporal graphs
● Graph methods in computer vision
● Human behavior and scene understanding
● Social networks analysis
● Data fusion methods in GNN
● Efficient and parallel computation for graph learning algorithms
● Reasoning over knowledge-graphs
● Interactivity, explainability and trust in graph-based learning
● Probabilistic graphical models
● Biomedical data analytics on graphs

Submission Guidelines
Authors are invited to submit original and unpublished papers of research 
and applications for this track. The author(s) name(s) and address(es) must 
not appear in the body of the paper, and self-reference should be in the 
third person. This is to facilitate double-blind review. Please, visit the 
website for more information about submission.

Journal Special Issue
The track committee is working to organize a journal Special Issue, to
which the authors of selected top papers of this track will be invited for
an extended version.

SAC No-Show Policy
Paper registration is required, allowing the inclusion of the paper/poster 
in the conference proceedings. An author or a proxy attending SAC MUST 
present the paper. This is a requirement for the paper/poster to be included 
in the ACM digital library. No-show of registered papers and posters will 
result in excluding them from the ACM digital library.
Last updated by Jianyi Lin in 2022-10-23
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