Multilayer network simplification: Approaches, models and methods

Interdonato Roberto, Magnani Matteo, Perna Diego, Tagarelli Andrea, Vega Davide. 2020. Multilayer network simplification: Approaches, models and methods. Computer Science Review, 36:100246, 20 p.

Journal article ; Article de synthèse ; Article de revue à facteur d'impact
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Abstract : Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their size, or because traditional methods defined to analyze simple networks do not have a straightforward extension able to handle multiple layers. Therefore, a number of methods have been devised in the literature to simplify multilayer networks with the objective of improving our ability to analyze them. In this article we provide a unified and practical taxonomy of existing simplification approaches, and we identify categories of multilayer network simplification methods that are still underdeveloped, as well as emerging trends.

Mots-clés Agrovoc : Analyse de système, Analyse de réseau

Classification Agris : U10 - Computer science, mathematics and statistics

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

Auteurs et affiliations

  • Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA)
  • Magnani Matteo, Uppsala University (SWE)
  • Perna Diego, University of Calabria (ITA)
  • Tagarelli Andrea, University of Calabria (ITA) - auteur correspondant
  • Vega Davide, Uppsala University (SWE)

Source : Cirad-Agritrop (

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