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SUMMARY:Structure Learning for Epidemic Processes on Graphs
DTSTART:20230713T134500
DTEND:20230713T150000
DTSTAMP:20260407T111502Z
UID:2abcfc89efeb18424c0e62f0ed2e0d13387e89772418f2a68fd9e3fb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Paula Mürmann\nEDIC candidacy exam\nExam president: Prof. Pas
 cal Frossard\nThesis advisor: Prof. Patrick Thiran\nCo-examiner: Prof. Yan
 ina Shkel\n\nAbstract\nAbstract—Structural inference is an essential tas
 k in modelling\nany kind of correlation data as a graphical network. We di
 scuss\nthe structure learning problem based on data obtained from\nepidemi
 c spreading processes on graphs\, where the infection state\nof a node dep
 ends on the state of its neighbours. We will link\nthis inference problem 
 to epidemic prediction problems such as\nthe influence maximisation proble
 m and discuss ideas to tailor\nthe learning approach to the prediction tas
 k.\n\nBackground papers\nDavid Kempe\, Jon Kleinberg\, and Éva Tardos (Au
 g. 24\, 2003). “Maximizing the spread\nof influence through a social net
 work”\nhttps://dl.acm.org/doi/10.1145/956750.956769\n\nManuel Gomez-Rodr
 iguez\, Jure Leskovec\, and Andreas Krause (Feb. 1\, 2012). “Inferring\n
 Networks of Diffusion and Influence”\nhttps://dl.acm.org/doi/10.1145/208
 6737.2086741\n\nMateusz Wilinski and Andrey Lokhov (July 1\, 2021). “Pre
 diction-Centric Learning of\nIndependent Cascade Dynamics from Partial Obs
 ervations\nhttps://proceedings.mlr.press/v139/wilinski21a.html\n 
LOCATION:BC 229 https://plan.epfl.ch/?room==BC%20229
STATUS:CONFIRMED
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