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SUMMARY:Edward de Brouwer (KU Leuven)
DTSTART:20221219T131500Z
DTEND:20221219T141500Z
DTSTAMP:20260423T010637Z
UID:MaML/3
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MaML/3/">Top
 ological Graph Neural Networks</a>\nby Edward de Brouwer (KU Leuven) as pa
 rt of Mathematics and Machine Learning\n\n\nAbstract\nGraph neural network
 s (GNNs) are a powerful architecture for tackling graph learning tasks\, y
 et have been shown to be oblivious to eminent substructures such as cycles
 . In this talk\, we introduce TOGL\, a novel layer that incorporates globa
 l topological information of a graph using persistent homology. TOGL can b
 e easily integrated into any type of GNN and is strictly more expressive (
 in terms the Weisfeiler–Lehman graph isomorphism test) than message-pass
 ing GNNs. Augmenting GNNs with TOGL leads to improved predictive performan
 ce for graph and node classification tasks\, both on synthetic data sets\,
  which can be classified by humans using their topology but not by ordinar
 y GNNs\, and on real-world data.\n
LOCATION:https://researchseminars.org/talk/MaML/3/
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