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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 multiples 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><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US> <span style='color:red'>Deadline Extended</span><o:p></o:p></span></p><p class=MsoNormal style='margin-left:106.2pt;text-indent:35.4pt'><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 37th ACM Symposium on Applied Computing (SAC 2022)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> April 25-29, 2022, Brno, Czech Republic<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> <a href="http://phuselab.di.unimi.it/GMLR2022">http://phuselab.di.unimi.it/GMLR2022</a> <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: <s>October 15, 2021</s><span style='color:red'> October 24, 2021</span><o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Notification of acceptance/rejection: December 10, 2021<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Camera-ready copies of accepted papers: December 21, 2021<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>SAC Conference: April 25 - 29, 2022<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><b><u><span lang=EN-US>Authors of selected top papers of this track will be asked to publish an extended version in a<o:p></o:p></span></u></b></p><p class=MsoNormal><b><u><span lang=EN-US>Special Issue of a good quoted International Journal</span></u></b><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>The ACM Symposium on Applied Computing (SAC 2022) has been a primary gathering forum<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>for applied computer scientists, computer engineers, software engineers, and application<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>developers from around the world. SAC 2022 is sponsored by the ACM Special Interest Group<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>on Applied Computing (SIGAPP), and will be held in Brno, Czech Republic. The technical track on<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Graph Models for Learning and Recognition (GMLR) is the first edition and is organized within<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>SAC 2022.<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 thanks to their<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>ability to encode both topological and semantic information. Encouraged by the success of CNNs, <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>a wide variety of methods have redefined the notion of convolution for graphs. <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>These new approaches have in general enabled effective training and achieved in <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>many cases better performances than competitors, though at the detriment of computational costs.<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>Typical examples of applications dealing with graph-based representation are: scene graph<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>generation, point clouds classification, and action recognition in computer vision; text<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>classification, inter-relations of documents or words to infer document labels in natural<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>language processing; forecasting traffic speed, volume or the density of roads in traffic<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>networks, whereas in 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 models for<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>learning and recognition tasks. GMLR spans, but is not limited to, 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 representationa 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=IT>Scientific Program Committee<o:p></o:p></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p><p class=MsoNormal><span lang=IT>Davide Boscaini<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Federico Castelletti<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Vittorio Cuculo<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Alessandro D’Amelio<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Gabriele Gianini<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Alessio Micheli<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Ryan A. Rossi<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Carlo Vercellis<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Naoufel Werghi<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>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 and applications for<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>this track. The author(s) name(s) and address(es) must not appear in the body of the paper, and<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>self-reference should be in the third person. This is to facilitate double-blind review. Please,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>visit the 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 in the conference<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>for the paper/poster to be included in the ACM digital library. No-show of registered papers<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>and posters will 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><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>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=EN-US>Jean-Yves Ramel (University of Tours)<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></div></body></html>