Back
ballester25a
Conference output

On the Expressivity of Persistent Homology in Graph Learning

BP2-STS

  • 2024
topological data analysis
topological deep learning
geometric deep learning
graph learning
expressivity
English Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order topological features, such as cycles of arbitrary length, in combination with multi-scale topological descriptors, has improved predictive performance for data sets with prominent topological structures, such as molecules. At the same time, the theoretical properties of persistent homology have not been formally assessed in this context. This paper intends to bridge the gap between computational topology and graph machine learning by providing a brief introduction to persistent homology in the context of graphs, as well as a theoretical discussion and empirical analysis of its expressivity for graph learning tasks.
Faculty
Faculté des sciences et de médecine
Department
Département d'Informatique
Language
  • English
Classification
Medicine, Technology, Engineering
Other electronic version

Published version

License
License undefined
Open access status
green
Persistent URL
https://folia.unifr.ch/unifr/documents/334049
Statistics
Document views: 11 File downloads:
  • ballester25a: 10