Name
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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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3110025.3116209 | ASONAM '17: Advances in Social Networks Analysis and Mining 2017
Sydney
Australia
July, 2017 |
Field | DocType | ISBN |
Data mining,Computer science,Social network analysis,Behavioural analysis,Artificial intelligence,STREAMS,Machine learning | Conference | 978-1-4503-4993-2 |
Citations | PageRank | References |
4 | 0.39 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thibaud Arnoux | 1 | 4 | 0.39 |
| Lionel Tabourier | 2 | 90 | 8.85 |
| Matthieu Latapy | 3 | 1488 | 103.74 |