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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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Contributor : Lionel Tabourier <>
Submitted on : Thursday, June 29, 2017 - 2:51:51 PM
Last modification on : Friday, January 8, 2021 - 5:32:05 PM
Long-term archiving on: : Thursday, January 18, 2018 - 1:34:13 AM


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  • HAL Id : hal-01550324, version 1


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. ⟨hal-01550324⟩



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