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DTSTART;TZID=Europe/Paris:20221216T093000
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DTSTAMP:20221208T154117Z
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SUMMARY:PhD defense: Kamrul Islam
DESCRIPTION:Kamrul Islam (Capsid) will defend his thesis\, entitled « Explainable link prediction in large complex graphs – application to drug repurposing »\, on Friday\, 16 December at 9.30 am in room B013. \nComposition du jury: \nRapporteurs: \nLuc Brun\, Professeur\, ENSICAEN\, France \nPaolo Merialdo\, Professeur\, Université Rome III (Roma Tre University)\, Italie \nExaminateurs: \nMiguel Couceiro\, Professeur\, Université de Lorraine\, France \nFatiha Saïs\, Professeure\, Université Paris Saclay\, France \nInvité : Marie-Dominique Devignes\, Chargée de Recherches\, CNRS\, France \nEncadrants: \nMalika Smaïl-Tabbone\, Maître de conférences\, Université de Lorraine\, HDR\, France \nSabeur Aridhi\, Maître de conférences\, Université de Lorraine\, France \nAbstract: \nLink prediction is one of the most interesting and long-standing problems in the field of graph mining; it predicts the probability of a link between two unconnected nodes. This thesis presents several contributions for link prediction in simple graphs and knowledge graphs. Firstly\, we compare a few similarity-based and embedding-based link prediction approaches in different simple graphs with diverse properties and analyze their interesting connections to alleviate the « black-box » limitation of embedding-based approaches. Secondly\, we develop an explainable supervised link prediction approach for simple graphs. Thirdly\, we develop a negative triple sampling method which are useful for training of embedding methods for knowledge graphs. Fourthly\, we develop a rule mining method for knowledge graphs and an explanation strategy using mined rules to explain embedding-based link predictions. Fifthly\, we apply our explainable link prediction approach to a biological knowledge graph for drug repurposing of COVID-19. Finally\, we present a new framework for distributed training of knowledge graph embedding methods.
URL:https://www.loria.fr/event/phd-defense-kamrul-islam/
LOCATION:B013
CATEGORIES:Soutenance
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