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UID:20260518T091910EDT-2943A52wSV@132.216.98.100
DTSTAMP:20260518T131910Z
DESCRIPTION:Community Detection in Degree-Corrected Block Models.\n\nCommun
 ity detection is a central problem of network data analysis. Given a netwo
 rk\, the goal of community detection is to partition the network nodes int
 o a small number of clusters\, which could often help reveal interesting s
 tructures. The present paper studies community detection in Degree-Correct
 ed Block Models (DCBMs). We first derive asymptotic minimax risks of the p
 roblem for a misclassification proportion loss under appropriate condition
 s. The minimax risks are shown to depend on degree correction parameters\,
  community sizes\, and average within and between community connectivities
  in an intuitive and interpretable way. In addition\, we propose a polynom
 ial time algorithm to adaptively perform consistent and even asymptoticall
 y minimax optimal community detection in DCBMs.\n
DTSTART:20161208T193000Z
DTEND:20161208T223000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Chao Gao\, Chicago
URL:https://www.mcgill.ca/mathstat/channels/event/chao-gao-chicago-264607
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