Combining structural and dynamic information to predict activity in link streams

Thibaud Arnoux 1 Lionel Tabourier 1 Matthieu Latapy 1
1 ComplexNetworks
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : 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.
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Communication dans un congrès
International Symposium on Foundations and Applications of Big Data Analytics, Aug 2017, Sydney, Australia. FAB 2017 Conference Proceedings 2017
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Contributeur : Lionel Tabourier <>
Soumis le : jeudi 29 juin 2017 - 14:51:51
Dernière modification le : vendredi 30 novembre 2018 - 01:29:27
Document(s) archivé(s) le : jeudi 18 janvier 2018 - 01:34:13

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Thibaud Arnoux, Lionel Tabourier, Matthieu Latapy. Combining structural and dynamic information to predict activity in link streams. International Symposium on Foundations and Applications of Big Data Analytics, Aug 2017, Sydney, Australia. FAB 2017 Conference Proceedings 2017. 〈hal-01550324〉

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