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[We apologize for multiple copies]
<p> </p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"
align="center">Call for Papers</p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"
align="center"><b>Graph Models for Learning and Recognition (GMLR)
Track</b></p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"
align="center">The 40th ACM Symposium on Applied Computing (SAC
2025)</p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"
align="center">March 31 - April 4, 2025 Catania, Italy</p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"
align="center"><a href="https://phuselab.di.unimi.it/GMLR2025/"
style="text-decoration:none;" class="moz-txt-link-freetext"
moz-do-not-send="true">https://phuselab.di.unimi.it/GMLR2025/</a></p>
<br>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><br>
</p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><b>Important
Dates</b></p>
<ul dir="ltr">
<li style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Submission
of regular papers and SRC abstracts: <b>September 20, 2024</b></li>
<li style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Notification
of papers/SRC acceptance/rejection: <b>October 30, 2024</b></li>
<li style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Camera-ready
copies of accepted papers/SRC: <b>November 29, 2024</b></li>
<li style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Author
registration due date: December 6, 2024</li>
</ul>
<p> </p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><br>
</p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><b>Motivations
and topics</b></p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">The ACM
Symposium on Applied Computing (SAC 2025) has been a primary
gathering forum for applied computer scientists, computer
engineers, software engineers, and application developers from
around the world. SAC 2025 is sponsored by the ACM Special
Interest Group on Applied Computing (SIGAPP), and will be held in
Catania, Italy. The technical track on Graph Models for Learning
and Recognition (GMLR) is the fourth edition and is organized
within SAC 2025. 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.</p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">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.</p>
<br>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">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:</p>
<ul>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Graph
Neural Networks: theory and applications</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Deep
learning on graphs</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Graph
or knowledge representational learning</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Graphs
in pattern recognition</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Graph
databases and linked data in AI</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Benchmarks
for GNN</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Dynamic,
spatial and temporal graphs</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Graph
methods in computer vision</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Human
behavior and scene understanding</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Social
networks analysis</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Data
fusion methods in GNN</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Efficient
and parallel computation for graph learning algorithms</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Reasoning
over knowledge-graphs</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Interactivity,
explainability and trust in graph-based learning</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Probabilistic
graphical models</p>
</li>
<li>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Biomedical
data analytics on graphs</p>
</li>
</ul>
<p><b style="font-weight:normal;"
id="docs-internal-guid-654284c3-7fff-8e85-34a1-97cc8818f3ab"> </b></p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><b
style="font-weight:normal;"
id="docs-internal-guid-654284c3-7fff-8e85-34a1-97cc8818f3ab"> </b></p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><b
style="font-weight:normal;"
id="docs-internal-guid-654284c3-7fff-8e85-34a1-97cc8818f3ab"><b>Submission
Guidelines</b></b></p>
<b style="font-weight:normal;"
id="docs-internal-guid-654284c3-7fff-8e85-34a1-97cc8818f3ab">
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">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.</p>
<br>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><b>SAC
No-Show Policy</b></p>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">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.</p>
</b>
<p dir="ltr"
style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><b>Track
Chairs</b></p>
<ul dir="ltr">
<li style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">Vittorio
Cuculo (University of Modena e Reggio Emilia)</li>
<li> Alessandro D'Amelio (University of Milan)</li>
<li> Giuliano Grossi (University of Milan)</li>
<li>Raffaella Lanzarotti (University of Milan)</li>
<li> Jianyi Lin (Università Cattolica del Sacro Cuore)</li>
</ul>
<p><b style="font-weight:normal;"
id="docs-internal-guid-654284c3-7fff-8e85-34a1-97cc8818f3ab"> </b></p>
<b style="font-weight:normal;"
id="docs-internal-guid-654284c3-7fff-8e85-34a1-97cc8818f3ab"> </b>
<pre class="moz-signature" cols="72">--
Vittorio Cuculo
Assistant Professor (RTD-A)
AImageLab - Dipartimento di Ingegneria "Enzo Ferrari"
Università degli Studi di Modena e Reggio Emilia
via Vivarelli 10, Modena, 41125, Italy
phone +390592056289</pre>
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