ACM SAC - Graph Models for Learning and Recognition GMLR on April 08-12, 2024 in Ávila, Spain

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 Genoa)

András Benczúr (Institute for Computer Science and Control)

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)

(others to be confirmed)

 

Important Dates

Submission of regular papers:    September 15, 2023

Notification of acceptance/rejection:     October 30, 2023

Camera-ready copies of accepted papers: November 30, 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.