In this tutorial, we will explore hypergraphs and how to leverage the Pythonic library PyTorch for learning from them, with a focus on making these concepts accessible to everyone, regardless of their background in computer science. Unlike traditional graphs, hypergraphs allow edges to connect any number of nodes, making them ideal for modeling complex group interactions that arise naturally in social networks, biological systems, and collaborative environments. The session will take the form of a hands-on workshop. Participants will receive the Jupyter notebook in advance and can follow along on their laptops, running the code simultaneously with the presenter. No prior knowledge of Python is required, though participants should have Jupyter installed on their devices. By the end of the tutorial, participants will understand what hypergraphs are and why they are important for representing multi-way relationships, learn how to represent and manipulate hypergraphs in Python using modern libraries, see how PyTorch enables machine learning on hypergraph structures through tensor operations and neural network architectures, and gain an introduction to the CHESHIRE algorithm, a state-of-the-art deep learning method for hyperlink prediction that employs Chebyshev spectral convolution to efficiently predict missing connections in complex networks and hypernetworks, including social and biological systems. The tutorial emphasizes intuition and practical implementation, providing hands-on toy examples drawn from real-world applications. Participants will leave with a clear understanding of both the theory and practice of hypergraph learning.

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Short Bio: Moses Boudourides is a retired Professor of Applied and Computational Mathematics from the University of Patras, Greece. Since his retirement in 2017, he has held visiting appointments at Linköping University in Sweden (Institute for Analytical Sociology, 2019), New York University Abu Dhabi (Science Division, 2019–2020), Haverford College (Department of Computer Science, 2021–2022), and Arizona State University (School of Public Affairs and School of Computing and Augmented Intelligence, 2022–2023). He has also been serving as an instructor in the Data Science Program at Northwestern University’s School of Professional Studies from 2019 through 2025. Earlier in his career, from 1982 to 1997, he was Associate Professor at the Department of Electrical and Computer Engineering of the Democritus University of Thrace in Xanthi, Greece, and Visiting Professor at the University of California, Irvine (Department of Mathematics, 1989–1990). He received his PhD from Johns Hopkins University, following undergraduate studies in Chemical Engineering at the National Technical University of Athens. In 2007, he organized the Sunbelt Social Networks Conference in Corfu, Greece. His current research focuses on higher-order social network analysis, computational social science, hypergraph analysis and hyperedge prediction, as well as the study of fair and unbiased algorithmic systems and responsible AI.