Connectionists: [CFP] [GMLR @ ACM SAC 2025] Technical track on Graph Models for Learning and Recognition

[Vittorio Cuculo]

[We apologize for multiple copies]

Call for Papers

*Graph Models for Learning and Recognition (GMLR) Track*

The 40th ACM Symposium on Applied Computing (SAC 2025)

March 31 - April 4, 2025 Catania, Italy

https://phuselab.di.unimi.it/GMLR2025/



*Important Dates*

  * Submission of regular papers and SRC abstracts: *September 20, 2024*
  * Notification of papers/SRC acceptance/rejection: *October 30, 2024*
  * Camera-ready copies of accepted papers/SRC: *November 29, 2024*
  * Author registration due date: December 6, 2024


*Motivations and topics*

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.

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.

*

*Track Chairs*

  * Vittorio Cuculo (University of Modena e Reggio Emilia)
  * Alessandro D'Amelio (University of Milan)
  * Giuliano Grossi (University of Milan)
  * Raffaella Lanzarotti (University of Milan)
  * Jianyi Lin (Università Cattolica del Sacro Cuore)

**

**

-- 
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
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