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X-WR-CALNAME:Providing Explanations for Unsupervised Graph Learning Models 
 - Hogun Park\, Associate Professor\, Sungkyunkwan University
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TZID:America/Vancouver
TZUNTIL:20270314T100000Z
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DTSTART:20241103T020000
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RDATE:20251102T020000
RDATE:20261101T020000
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DTSTAMP:20251218T212748Z
CLASS:PUBLIC
CREATED:20250707T191503Z
DESCRIPTION:Abstract: This talk presents advancements in providing explanat
 ions for unsupervised graph learning models. It highlights eXplainable AI 
 (XAI)'s role in identifying influential subgraphs for graph learning model
 s. A key contribution\, the UNR-Explainer (ICLR 2024) \, generates counter
 factual explanations for unsupervised node representation learning models 
 by using Monte Carlo Tree Search to find important subgraphs. Additionally
 \, the talk introduces the HINT-G framework (upcoming KDD 2025) \, which l
 everages influence functions to explain GNNs across supervised and unsuper
 vised settings…
DTSTART;TZID=America/Vancouver:20250715T164500
DTEND;TZID=America/Vancouver:20250715T174500
LAST-MODIFIED:20250707T191940Z
LOCATION:UBC Vancouver Campus\, Fried Kaiser (KAIS) building\, Room 2020/20
 30\, 2332 Main Mall
SUMMARY:Providing Explanations for Unsupervised Graph Learning Models - Hog
 un Park\, Associate Professor\, Sungkyunkwan University
TRANSP:OPAQUE
URL:https://www.dfp.ubc.ca/event/providing-explanations-unsupervised-graph-
 learning-models-hogun-park-associate-professor
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