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Feature-rich networks: Going beyond complex network topologies

Interdonato Roberto, Atzmueller Martin, Gaito Sabrina, Kanawati Rushed, Largeron Christine, Sala Alessandra. 2019. Feature-rich networks: Going beyond complex network topologies. Applied Network Science, 4:4, 13 p.

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Résumé : The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilistic Networks, and many other task-driven and data-driven models. In this paper, we propose an overview of these models and their main applications, described under the common denomination of Feature-rich Networks, i. e. models where the expressive power of the network topology is enhanced by exposing one or more peculiar features. The aim is also to sketch a scenario that can inspire the design of novel feature-rich network models, which in turn can support innovative methods able to exploit the full potential of mining complex network structures in domain-specific applications.

Mots-clés Agrovoc : analyse de réseau, topologie, modèle mathématique, analyse de données

Mots-clés libres : Analyse de réseaux, Réseaux complexes, Feature-rich networks, Réseaux multicouche

Classification Agris : U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 7 (2019-) - Hors champs stratégiques

Auteurs et affiliations

  • Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0002-0536-6277 - auteur correspondant
  • Atzmueller Martin, University of Tilburg (NLD)
  • Gaito Sabrina, Università degli studi di Milano (ITA)
  • Kanawati Rushed, Université Sorbonne Paris Cité (FRA)
  • Largeron Christine, Université de Lyon (FRA)
  • Sala Alessandra, Nokia Bell Labs (IRL)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/597769/)

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