Predicting links in ego-networks using temporal information


Lionel Tabourier, Anne-Sophie Libert and Renaud Lambiotte

In EPJ Data Science (2016) 5: 1


Link prediction appears as a central problem of network science, as
it calls for unfolding the mechanisms that govern the micro-dynamics
of the network. In this work, we are interested in ego-networks, that is
the mere information of interactions of a node to its neighbors, in the
context of social relationships. As the structural information is very
poor, we rely on another source of information to predict links among
egos’ neighbors: the timing of interactions. We define several features
to capture different kinds of temporal information and apply machine
learning methods to combine these various features and improve the
quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features
which prove themselves to perform well in this context, in particular
the temporal profile of interactions and elapsed time between contacts.

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