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UID:374@lincs.fr
DTSTART;TZID=Europe/Paris:20180514T120000
DTEND;TZID=Europe/Paris:20180514T123000
DTSTAMP:20180515T060249Z
URL:https://www.lincs.fr/events/modularity-for-soft-graph-clustering/
SUMMARY:Modularity for soft-graph clustering
DESCRIPTION:Clustering is a central problem in machine learning for which
 graph-based approaches have proven their efficiency. In this paper\, we
 study a relaxation of the modularity maximization problem\, well-known in
 the graph partitioning literature. A solution of this relaxation gives to
 each element of the dataset a probability to belong to a given cluster\,
 whereas a solution of the standard modularity problem is a simple
 partition. We introduce an efficient optimization algorithm to solve this
 relaxation\, that is both memory efficient and local\, and show that our
 method includes the Louvain algorithm\, a state-of-the-art technique to
 solve the traditional modularity problem. Experiments on both synthetic and
 real-world data show that our approach provides meaningful information on
 various types of data.
CATEGORIES:Seminars,Youtube
LOCATION:LINCS / EIT Digital\, 23 avenue d'Italie\, 75013 Paris\, France
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 Paris\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS / EIT Digital:geo:0,0
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TZID:Europe/Paris
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DTSTART:20180325T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
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