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Thibaud ARNOUX
État académique
Thèse en cours...
Sujet: Combiner prédiction de liens et détection d'événements pour décrire la dynamique des flots de liens
Direction de thèse:
Encadrement de thèse:
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
Combining structural and dynamic information to predict activity in link streams
International audience
A link stream is a sequence of triplets (t, u, v) meaning that nodes u and v have interacted at time t. Capturing both the structural and temporal aspects of interactions is crucial for many real world datasets like contact between individuals. We tackle the issue of activity prediction in link streams, that is to say predicting the number of links occurring during a given period of time and we present a protocol that takes advantage of the temporal and structural information contained in the link stream. We introduce a way to represent the information captured using different features and combine them in a prediction function which is used to evaluate the future activity of links.
FAB 2017 Conference Proceedings International Symposium on Foundations and Applications of Big Data Analytics https://hal.archives-ouvertes.fr/hal-01550324 International Symposium on Foundations and Applications of Big Data Analytics, Aug 2017, Sydney, Australia. FAB 2017 Conference Proceedings 2017ARRAY(0x7f03ffbb1a68) 2017-08