Thibaud Arnoux ; Lionel Tabourier ; Matthieu Latapy - Predicting interactions between individuals with structural and dynamical information

jimis:5639 - Journal of Interdisciplinary Methodologies and Issues in Sciences, 23 juillet 2019, Vol. 5 - Analyse de graphes et réseaux - https://doi.org/10.18713/JIMIS-150719-5-3
Predicting interactions between individuals with structural and dynamical information

Auteurs : Thibaud Arnoux ; Lionel Tabourier ; Matthieu Latapy

    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.


    Volume : Vol. 5 - Analyse de graphes et réseaux
    Publié le : 23 juillet 2019
    Accepté le : 23 juillet 2019
    Soumis le : 23 juillet 2019
    Mots-clés : Link stream,Activity prediction,Link prediction,Real-world networks,[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI],[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
    Financeurs :
      Source : OpenAIRE Research Graph
    • Algodiv: Recommandation algorithmique et diversité des informations du web; Financeur: French National Research Agency (ANR); Code: ANR-15-CE38-0001

    Partager

    Statistiques de consultation

    Cette page a été consultée 418 fois.
    Le PDF de cet article a été téléchargé 321 fois.