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UID:DSC-22103
DTSTART;TZID=Europe/Berlin:20250714T130000
SEQUENCE:1752471390
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20250714T140000
URL:https://www.dresden-science-calendar.de/calendar/en/detail/22103
LOCATION:MPI-CBG\, Pfotenhauerstraße 10801307 Dresden
SUMMARY:Schindler: Topological autocorrelation in multiscale clusterings vi
 a 2-parameter persistent homology
CLASS:PUBLIC
DESCRIPTION:Speaker: Juni Schindler\nInstitute of Speaker: Imperial College
  London\nTopics:\n\n Location:\n  Name: MPI-CBG (MPI-CBG CSBD SR Ground Fl
 oor (VC))\n  Street: Pfotenhauerstraße 108\n  City: 01307 Dresden\n  Phon
 e: +49 351 210-0\n  Fax: +49 351 210-2000\nDescription: In many areas of d
 ata science\, networks have an intrinsic multiscale structure\, whereby me
 aningful descriptions exist at different levels of coarseness (or scales).
  In such cases\, it is desirable to go beyond clustering into a single par
 tition to find a (not necessarily hierarchical) sequence of partitions tha
 t captures aspects of the data at multiple levels of resolution. A natural
  problem is then to analyse and characterise such sequences of coarsening 
 multiscale clusterings. To enhance the interpretability for multiscale clu
 sterings\, we use multi-parameter persistent homology (MPH) and introduce 
 a 2-parameter filtration of abstract simplicial complexes\, termed Multisc
 ale Clustering Bifiltration (MCbiF). The MCbiF captures the “topological
  autocorrelation” of the history-dependent\, non-stationary sequence of 
 non-hierarchical\, coarsening partitions\, and leads to rich algebraic inv
 ariants like the 0- and 1-dimensional Hilbert functions. We illustrate the
  MCbiF with an application to ensembles of graphs sampled from stochastic 
 block models with different planted partition structure and find that our 
 method both captures the hierarchy of the models and recovers the ground-t
 ruth scales as “conflict-resolving” partitions. To our knowledge\, MCb
 iF is the first application of multiparameter persistence to analyse multi
 scale networks. By disentangling the “aggregation history” in multisca
 le clusterings\, MCbiF provides enhanced sensitivity and robustness to cha
 racterise the higher-order interactions in non-hierarchical sequences of p
 artitions.
DTSTAMP:20261010T084053Z
CREATED:20250705T053823Z
LAST-MODIFIED:20250714T053630Z
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