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, July 23, 2019, Vol. 5 - Graph and network analysis -
Predicting interactions between individuals with structural and dynamical informationArticle

Authors: Thibaud Arnoux 1; Lionel Tabourier 1; Matthieu Latapy ORCID1

  • 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.

Volume: Vol. 5 - Graph and network analysis
Section: Subject Area 3: Graphs and Networks
Published on: July 23, 2019
Accepted on: July 23, 2019
Submitted on: July 23, 2019
Keywords: 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]
    Source : OpenAIRE Graph
  • Algodiv: Recommandation algorithmique et diversité des informations du web; Funder: French National Research Agency (ANR); Code: ANR-15-CE38-0001

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