Predicting interactions between individuals with structural and dynamical information

Thibaud Arnoux (1) , Lionel Tabourier (1) , Matthieu Latapy (1)

1 ComplexNetworks

Capturing both structural and temporal features of interactions is crucial in many real-world situations like studies of contact between individuals. Using the link stream formalism to model data, we address here the activity prediction problem: we predict the number of links that will occur during a given time period between each pair of nodes. To do this, we take benefit from the temporal and structural information captured by link streams. We design and implement a modular supervised learning method to make prediction, and we study the key elements influencing its performances. We then introduce classes of node pairs, which improves prediction quality and increases diversity.

Mots clés: Activity prediction; Real-world networks; Link prediction; Link stream

Domaines: Réseaux sociaux et d'information [cs.SI]; Machine Learning [stat.ML]

Thibaud Arnoux, Lionel Tabourier, Matthieu Latapy. Predicting interactions between individuals with structural and dynamical information. Journal of Interdisciplinary Methodologies and Issues in Science, 2019, Graph and network analysis, Analysis of networks and graphs, pp.3. ⟨10.18713/JIMIS-150719-5-3⟩. ⟨hal-02191126⟩