Submission deadline EXTENDED to: October 13, 2023

Call for Papers

Graph Models for Learning and Recognition (GMLR) Track
The 39th ACM Symposium on Applied Computing (SAC 2024)
April 8-12, 2024, Avila, Spain
https://phuselab.di.unimi.it/GMLR2024

Track Chairs
============
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 Genova)
András Benczúr (Institute for Computer Science and Control)
Laura-Bianca Bilius (University of Suceava)
Sathya Bursic (University of Milano-Bicocca)
Antonella Carbonaro (University of Bologna)
Vittorio Cuculo (University of Modena and Reggio Emilia)
Samuel Feng (Sorbonne University Abu Dhabi)
Gabriele Gianini (University of Milan)
Francesco Isgrò (University of Naples Federico II)
Sotirios Kentros (Salem State University)
Giosuè Lo Bosco (University of Palermo)
Maurice Pagnucco (University of New South Wales)
Sabrina Patania (University of Milan)
Alessandro Provetti (Birkbeck University of London)
Jean-Yves Ramel (University of Tours)
Ryan A. Rossi (Adobe Research)
Alessandro Sperduti (University of Padua)

Important Dates
===============
Submission of regular papers: EXTENDED to October 13, 2023
Notification of acceptance/rejection: November 17, 2023
Camera-ready copies of accepted papers: December 15, 2023
SAC Conference: April 8 - 12, 2024

Motivations and topics
======================
The ACM Symposium on Applied Computing (SAC 2024) has been a primary gathering
forum for applied computer scientists, computer engineers, software engineers,
and application developers from around the world. SAC 2024 is sponsored by the
ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in
Avila, Spain. The technical track on Graph Models for Learning and
Recognition (GMLR) is the third edition and is organized within SAC 2024.
Graphs have gained a lot of attention in the pattern recognition community
thanks to their ability to encode both topological and semantic information.
Despite their invaluable descriptive power, their arbitrarily complex
structured nature poses serious challenges when they are involved in learning
systems. Some (but not all) of challenging concerns are: a non-unique
representation of data, heterogeneous attributes (symbolic, numeric, etc.),
and so on.
In recent years, due to their widespread applications, graph-based learning
algorithms have gained much research interest. Encouraged by the success of
CNNs, a wide variety of methods have redefined the notion of convolution and
related operations on graphs. These new approaches have in general enabled
effective training and achieved in many cases better performances than
competitors, though at the detriment of computational costs.
Typical examples of applications dealing with graph-based representation are:
scene graph generation, point clouds classification, and action recognition in
computer vision; text classification, inter-relations of documents or words to
infer document labels in natural language processing; forecasting traffic
speed, volume or the density of roads in traffic networks, whereas in
chemistry researchers apply graph-based algorithms to study the graph
structure of molecules/compounds.

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.

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.