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Combining structural and dynamic information to predict activity in link streams
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.
Cited literature [15 references]
https://hal.archives-ouvertes.fr/hal-01550324
Contributor : Lionel Tabourier Connect in order to contact the contributor
Submitted on : Thursday, June 29, 2017 - 2:51:51 PM
Last modification on : Tuesday, November 16, 2021 - 5:22:38 AM
Long-term archiving on: : Thursday, January 18, 2018 - 1:34:13 AM
Contributor : Lionel Tabourier Connect in order to contact the contributor
Submitted on : Thursday, June 29, 2017 - 2:51:51 PM
Last modification on : Tuesday, November 16, 2021 - 5:22:38 AM
Long-term archiving on: : Thursday, January 18, 2018 - 1:34:13 AM
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