BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Xavier Bresson (Nanyang Technological University) DTSTART:20210127T110000Z DTEND:20210127T120000Z DTSTAMP:20260423T003240Z UID:MPML/25 DESCRIPTION:Title: Be nchmarking Graph Neural Networks\nby Xavier Bresson (Nanyang Technolog ical University) as part of Mathematics\, Physics and Machine Learning (IS T\, Lisbon)\n\n\nAbstract\nGraph neural networks (GNNs) have become the st andard toolkit for analyzing and learning from data on graphs. As the fiel d grows\, it becomes critical to identify key architectures and validate n ew ideas that generalize to larger\, more complex datasets. Unfortunately\ , it has been increasingly difficult to gauge the effectiveness of new mod els in the absence of a standardized benchmark with consistent experimenta l settings. In this work\, we introduce a reproducible GNN benchmarking fr amework\, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman G NNs (WL-GNNs) compared to message passing-based graph convolutional networ ks (GCNs) for a variety of graph tasks with medium-scale datasets.\n LOCATION:https://researchseminars.org/talk/MPML/25/ END:VEVENT END:VCALENDAR