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UID:796@lincs.fr
DTSTART;TZID=Europe/Paris:20231108T103000
DTEND;TZID=Europe/Paris:20231108T113000
DTSTAMP:20231121T134744Z
URL:https://www.lincs.fr/events/the-projection-method/
SUMMARY:The Projection Method: A Unified Framework for Community Detection
DESCRIPTION:We present the class of projection methods for community
 detection that generalizes many popular community detection methods. In
 this framework\, we represent each clustering (partition) by a vector on a
 high-dimensional hypersphere. A community detection method is a projection
 method if it can be described by the following two-step approach:\n\n 	the
 graph is mapped to a query vector on the hypersphere\;\n 	the query vector
 is projected to the set of clustering vectors.\n\nThis last projection step
 is performed by minimizing the distance between the query vector and the
 clustering vector\, over the set of clusterings. This setup generalizes
 many popular community detection methods\, including the maximization of
 modularity\, Markov stability and the likelihood of several models.\n\nWe
 show that these different methods suffer from the same granularity problem:
 they have parameters that control the granularity of the resulting
 clustering\, but choosing these to obtain clusterings of the desired
 granularity is nontrivial. We provide a general heuristic to address this
 granularity problem\, which can be applied to any projection method.
 Finally\, we show how\, given a generator of graphs with community
 structure\, we can optimize a projection method for this generator in order
 to obtain a community detection method that performs well on this
 generator.
CATEGORIES:Network Theory,Working Group,Youtube
LOCATION:Room 4B01\, 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=Room 4B01:geo:0,0
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TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
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DTSTART:20231029T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
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