Call for Papers

   Graph Models for Learning and Recognition (GMLR) Track

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

                              April 25-29, 2022, Brno, Czech Republic

                              http://phuselab.di.unimi.it/GMLR2022

Track Chairs

Donatello Conte (University of Tours)

Giuliano Grossi (University of Milan)

Raffaella Lanzarotti (University of Milan)

Jianyi Lin (Università Cattolica del Sacro Cuore)

Jean-Yves Ramel (University of Tours)

Scientific Program Committee (in progress)

Davide Boscaini (Fondazione Bruno Kessler)

Ryan A. Rossi     (Adobe Research)

…

Important Dates

Submission of regular papers:    October 15, 2021

Notification of acceptance/rejection:     December 10, 2021

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

SAC Conference:              April 25 – 29, 2022

Motivations and topics

The ACM 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 (GMLR) is the first edition and is 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.

Encouraged by the success of CNNs, a wide variety of methods

have redefined the notion of convolution for 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 representationa 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

Authors of selected top papers of this track will be asked to publish an extended version in a

Special Issue of a Journal (the journal will be announced soon).

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.