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</o:shapelayout></xml><![endif]--></head><body lang=FR link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal><span lang=EN-US>---------------------------------------<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Apologies for multiple copies<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>---------------------------------------<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Call for Papers<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Graph Models for Learning and Recognition (GMLR) Track<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>The 38th ACM Symposium on Applied Computing (SAC 2023)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> March 27 - April 2, 2023, Tallinn, Estonia<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> http://phuselab.di.unimi.it/GMLR2023<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Important Dates<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>===============<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Submission of regular papers: October 1, 2022<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Notification of acceptance/rejection: November 19, 2022<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Camera-ready copies of accepted papers: December 6, 2022<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>SAC Conference: March 27 - April 2, 2023<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Motivations and topics<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>======================<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>The ACM Symposium on Applied Computing (SAC 2023) has been a primary gathering <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>forum for applied computer scientists, computer engineers, software engineers, <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>and application developers from around the world. SAC 2023 is sponsored by the <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Tallinn, Estonia. The technical track on Graph Models for Learning and <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Recognition (GMLR) is the second edition and is organized within SAC 2023.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Graphs have gained a lot of attention in the pattern recognition community <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>thanks to their ability to encode both topological and semantic information. <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Despite their invaluable descriptive power, their arbitrarily complex <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>structured nature poses serious challenges when they are involved in learning <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>systems. Some (but not all) of challenging concerns are: a non-unique <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>representation of data, heterogeneous attributes (symbolic, numeric, etc.), <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>and so on.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>In recent years, due to their widespread applications, graph-based learning <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>algorithms have gained much research interest. Encouraged by the success of <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>CNNs, a wide variety of methods have redefined the notion of convolution and <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>related operations on graphs. These new approaches have in general enabled <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>effective training and achieved in many cases better performances than <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>competitors, though at the detriment of computational costs. <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Typical examples of applications dealing with graph-based representation are: <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>scene graph generation, point clouds classification, and action recognition in <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>computer vision; text classification, inter-relations of documents or words to <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>infer document labels in natural language processing; forecasting traffic <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>speed, volume or the density of roads in traffic networks, whereas in <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>chemistry researchers apply graph-based algorithms to study the graph <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>structure of molecules/compounds.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>This track intends to focus on all aspects of graph-based representations and <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>models for learning and recognition tasks. GMLR spans, but is not limited to, <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>the following topics:<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>● Graph Neural Networks: theory and applications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Deep learning on graphs<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Graph or knowledge representational learning<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Graphs in pattern recognition<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Graph databases and linked data in AI<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Benchmarks for GNN<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Dynamic, spatial and temporal graphs<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Graph methods in computer vision<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Human behavior and scene understanding<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Social networks analysis<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Data fusion methods in GNN<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Efficient and parallel computation for graph learning algorithms<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Reasoning over knowledge-graphs<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Interactivity, explainability and trust in graph-based learning<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Probabilistic graphical models<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>● Biomedical data analytics on graphs<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Authors of selected top papers of this track will be asked to publish an <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>extended version in a Special Issue of a high-impact Journal (the journal <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>will be announced later).<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Track Chairs<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>============<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Donatello Conte (University of Tours)<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Alessandro D'Amelio (University of Milan)<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Giuliano Grossi (University of Milan)<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Raffaella Lanzarotti (University of Milan)<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Jianyi Lin (Università Cattolica del Sacro Cuore)<o:p></o:p></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Scientific Program Committee<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>============================<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Annalisa Barla (University of Genoa)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Davide Boscaini (Bruno Kessler Foundation)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Vittorio Cuculo (University of Milan)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Samuel Feng (Sorbonne University)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Gabriele Gianini (University of Milan)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Alessio Micheli (University of Pisa)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Carlos Oliver (ETH Zürich)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Maurice Pagnucco (University of New South Wales)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Ryan A. Rossi (Adobe Research)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Jean-Yves Ramel (University of Tours)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>(others to be confirmed)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Submission Guidelines<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>=====================<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Authors are invited to submit original and unpublished papers of research <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>and applications for this track. The author(s) name(s) and address(es) must <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>not appear in the body of the paper, and self-reference should be in the <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>third person. This is to facilitate double-blind review. Please, visit the <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>website for more information about submission.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>SAC No-Show Policy<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>==================<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Paper registration is required, allowing the inclusion of the paper/poster <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>in the conference proceedings. An author or a proxy attending SAC MUST <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>present the paper. This is a requirement for the paper/poster to be included <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>in the ACM digital library. No-show of registered papers and posters will <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>result in excluding them from the ACM digital library.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p></div></body></html>