Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)
Das Suchergebnis hat sich seit Ihrer Suchanfrage verändert. Eventuell werden Dokumente in anderer Reihenfolge angezeigt.
  • Treffer 19 von 25
Zurück zur Trefferliste

Koopman-based spectral clustering of directed and time-evolving graphs

  • While spectral clustering algorithms for undirected graphs are well established and have been successfully applied to unsupervised machine learning problems ranging from image segmentation and genome sequencing to signal processing and social network analysis, clustering directed graphs remains notoriously difficult. Two of the main challenges are that the eigenvalues and eigenvectors of graph Laplacians associated with directed graphs are in general complex-valued and that there is no universally accepted definition of clusters in directed graphs. We first exploit relationships between the graph Laplacian and transfer operators and in particular between clusters in undirected graphs and metastable sets in stochastic dynamical systems and then use a generalization of the notion of metastability to derive clustering algorithms for directed and time-evolving graphs. The resulting clusters can be interpreted as coherent sets, which play an important role in the analysis of transport and mixing processes in fluid flows.

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Statistik - Anzahl der Zugriffe auf das Dokument
Metadaten
Verfasserangaben:Stefan KlusORCiD, Natasa Djurdjevac ConradORCiD
Dokumentart:Artikel
Titel des übergeordneten Werkes (Englisch):Journal of Nonlinear Science
Band:33
Jahr der Erstveröffentlichung:2022
Seitenzahl:8
ArXiv-Id:http://arxiv.org/abs/2204.02951
DOI:https://doi.org/10.1007/s00332-022-09863-0
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.