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GMLR @ ACM SAC 2025 : ACM SAC Track on Graph Models for Learning and Recognition | |||||||||||||||
Link: https://phuselab.di.unimi.it/GMLR2025 | |||||||||||||||
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Call For Papers | |||||||||||||||
Call for Papers
Graph Models for Learning and Recognition (GMLR) Track The 40th ACM Symposium on Applied Computing (SAC 2025) March 31 - April 4, 2025, Catania, Italy https://phuselab.di.unimi.it/GMLR2025 Track Chairs ============ Vittorio Cuculo (University of Modena e Reggio Emilia) 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 Genova) András Benczúr (Institute for Computer Science and Control) Laura-Bianca Bilius (University of Suceava) 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 Milano-Bicocca) 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) (provisional list) Important Dates =============== Submission of regular papers: September 20, 2024 Notification of acceptance/rejection: October 30, 2024 Camera-ready copies of accepted papers: November 29, 2024 SAC Conference: March 31 - April 4, 2025 Motivations and topics ====================== The ACM Symposium on Applied Computing (SAC 2025) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2025 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in Catania, Italy. The technical track on Graph Models for Learning and Recognition (GMLR) is the third edition and is organized within SAC 2025. 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. |
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