BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.2.3.1//EN
BEGIN:VEVENT
UID:851@lincs.fr
DTSTART;TZID=Europe/Paris:20241025T110000
DTEND;TZID=Europe/Paris:20241025T120000
DTSTAMP:20241028T133736Z
URL:https://www.lincs.fr/events/hierarchical-community-detection-in-hierar
 chical-stochastic-block-models/
SUMMARY:Hierarchical Community Detection in Hierarchical Stochastic Block
 Models
DESCRIPTION:In this session of our reading group\, I will discuss community
 detection in hierarchical clustering of networks\, based on the
 paper "When Does Bottom-up Beat Top-down in Hierarchical Community
 Detection?" by Maximilien Dreveton et al. Hierarchical clustering involves
 constructing a tree of communities\, with lower levels revealing
 finer-grained structures. Two main approaches address this problem:
 divisive (top-down) algorithms\, which recursively split nodes\, and
 agglomerative (bottom-up) algorithms\, which start by identifying the
 smallest communities and then merge them using a linkage method. This talk
 will focus on establishing theoretical guarantees for the exact recovery of
 the hierarchical tree under a Hierarchical Stochastic Block Model (HSBM)
 using a bottom-up algorithm. The findings show that bottom-up methods can
 achieve the information-theoretic threshold for exact recovery at
 intermediate hierarchy levels\, offering less restrictive conditions than
 top-down algorithms and expanding the feasible region for accurate
 community detection.
CATEGORIES:Network Theory,Working Group,Youtube
LOCATION:Amphi 6\, 19 Place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 Place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi 6:geo:0,0
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:DAYLIGHT
DTSTART:20240331T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
END:DAYLIGHT
END:VTIMEZONE
END:VCALENDAR