Graph Models for Learning and Recognition 
(GMLR 2022) 

The 37th ACM Symposium on Applied Computing (SAC 2022)

Brno, Czech Republic
April 25 - 29, 2022

GMLR 2022 Track

The 37th ACM/SIGAPP Symposium on Applied Computing (SAC 2022) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2022 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in Brno, Czech Republic.

The technical track on Graph Models for Learning and Recognition is at its first edition and will be organized within SAC 2022.

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.

The complexity of graph data carries significant challenges for existing algorithms. Some (but not all) of challenging concerns are: a non-unique representation of data, heterogeneous attributes (symbolic, numeric, etc.), and so on. Furthermore, in the machine learning context, even other important issues are addressed. For example, as graphs can be irregular, a graph may have a variable size of unordered nodes, and the nodes may have a different number of neighbors, resulting in some important operations (e.g., convolutions) that are easy to compute in a vector domain, but difficult to apply to the graph domain.

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 for graphs and provided, in particular, a suitable representation for ubiquitous spatio-temporal data. 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 as a whole intends to focus on all aspects of graph-based representations and models for learning and recognition tasks.

Topics include but are not limited to
  - 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       

Important dates

Submission of regular papers and SRC abstracts:  October 15, 2021

Notification of acceptance/rejection:                         December 10, 2021

Camera-ready copies of accepted papers/SRC:     December 21, 2021

Conference:                                                                       April 25 - 29, 2022

Tuesday April 26, 2022: SRC Posters Exhibit
Wednesday April 27, 2022: Non-SRC Posters Program
Thursday April 28, 2022: SRC Oral Presentations


Original and unpublished papers are solicited from the above-mentioned areas. The file format should be PDF. The author(s) name(s) and address(es) must not appear in the paper, and self reference should be in the third person. This is to facilitate double blind review. Papers must be formatted according to the template which is available at the SAC 2022 website.

Submission system
Original manuscripts should be submitted in electronic format through the START system used by SAC 2022 conference.
Author kit is here.
Submission page is here.

Student Research Competition (SRC)
Graduate students are invited to submit research abstracts (maximum of 4 pages in ACM camera-ready format) following the instructions published at SAC 2022 website. Submission of the same abstract to multiple tracks is not allowed. All research abstract submissions will be reviewed by researchers and practitioners with expertise in the track focus area to which they are submitted.
Authors of selected abstracts will have the opportunity to give poster and oral presentations of their work and compete for three top-winning places. The SRC committee will evaluate and select First, Second, and Third place winners. The winners will receive medals and cash awards. Winners will be announced during the conference banquet. Invited students receive SRC travel support (US$500) and are eligible to apply to the SIGAPP Student Travel Award Program (STAP) for additional travel support.
Please submit your research abstracts electronically to the SRC START submission system in PDF format.

Call for SRC proposal 
Submission page for SRC is here

Paper size limits and other constraints
The length of a regular paper is 8 page (included in the registration) + 2 pages (at extra charge) = 10 pages maximum.
The length of a poster is 3 pages (included in the registration) + 1 page (at extra charge) = 4 pages maximum.
The length of SRC abstracts is 4 pages maximum.
A paper cannot be sent to more than one track.

SAC No-Show Policy
Paper registration is required, allowing the inclusion of the papers and posters in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for all accepted papers, posters, and invited SRC submissions to be included in the ACM digital library. No-show of scheduled papers, posters, and student research abstracts will result in excluding them from the ACM digital library.

Accepted papers will be published in the annual conference proceedings and will be included in the ACM digital library. Paper registration is required, allowing the inclusion of the paper, poster, or SRC abstract in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for including the work in the ACM/IEEE digital library. No-show of registered papers, posters, and SRC abstracts will result in excluding them from the ACM/IEEE digital library.

Track Co-Chairs

Donatello Conte University of Tours, Computer Science Laboratory (LIFAT) -

Giuliano Grossi University of Milan, Computer Science Department -

Raffaella Lanzarotti University of Milan, Computer Science Department -

Jianyi Lin Università Cattolica del Sacro Cuore, Department of Statistical Science -

Jean-Yves Ramel University of Tours, Computer Science Laboratory (LIFAT) -

Program Committee

Davide Boscaini                  Fondazione Bruno Kessler

Federico Castelletti           Università Cattolica del Sacro Cuore

Vittorio Cuculo                    Università degli Studi di Milano

Alessandro D’Amelio          Università degli Studi di Milano

Samuel Feng                        Khalifa University

Gabriele Gianini                   Università degli Studi di Milano

Alessio Micheli                    Politecnico di Milano

Maurice Pagnucco            University of New South Wales

Ryan A. Rossi                      Adobe Research

Carlo Vercellis                    Politecnico di Milano

Naoufel Werghi                  Khalifa University

Yoshitaka Arahori             Tokyo Institute of Technology

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