SPS-DSI (DEGAS) Webinar: Vertex, Edge, Clique: What's in a Graph?
Date: 20 November 2024
Time: 2:00 PM (Paris Time)
Presenter(s): Bastian Grossenbacher Rieck
DEGAS Webinar Series is an event initiated by the Data Science Initiative (DSI) of the IEEE Signal Processing (SP) Society. The goal is to provide the SP community with updates and advances in learning and inference on graphs. Signal processing and machine learning often deal with data living in regular domains such as space and time. This webinar series will cover the extension of these methods to network data, including topics such as graph filtering, graph sampling, spectral analysis of network data, graph topology identification, geometric deep learning, and so on. Applications can for instance be found in image processing, social networks, epidemics, wireless communications, brain science, recommender systems, and sensor networks. These bi-weekly webinars will be hosted on Zoom, with recordings made available in the IEEE Signal Processing Society’s YouTube channel following the live events. Further details about live and streaming access will follow. Each webinar speaker will give a lecture, which is followed by Q&A and discussions.
Abstract
Graph learning is one of the most rapidly growing subfields of machine learning research. With a deluge of different architectures available, one may get the impression that anything can be modelled as a graph. However, for some data sets, it turns out that structural features are not driving predictive performance, and the existence of edges may not even be beneficial for generalization, while for other data sets, edge and higher-order elements like cliques are critical for obtaining good predictive performance. These puzzling findings prompt me to ponder in which direction our field should move and raise questions about how we work with such data in practice.
Biography
Bastian Grossenbacher Rieck received the M.Sc. degree in mathematics, as well as the Ph.D. in computer science, from Heidelberg University in Germany.
He is a Full Professor of Machine Learning at the University of Fribourg in Switzerland and the Principal Investigator of the AIDOS Lab, focusing on geometrical-topological methods in machine learning. Bastian is also a member of ELLIS, the European Laboratory for Learning and Intelligent Systems. Wearing yet another hat, he serves as the co-director of the Applied Algebraic Topology Research Network.
Dr. Rieck is a big proponent of scientific outreach and enjoys blogging about his research, academia in general, and software development.
