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Modeling, analyzing and mining feature-rich networks

Pixabay, CC0 Creative CommonsThe growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations. The design of innovative complex approaches is the key to enable a deeper understanding of feature-rich networks, i.e., network models exposing specific features able to enhance their expressive power. Some examples of feature-rich networks are Multilayer Networks, Temporal and Heterogeneous Networks, Knowledge Graphs, Probabilistic Networks and generic Attributed Graphs. In this scenario, the need to expose and process domain-specific features when facing critical real-world tasks can prompt researchers to exploit the full potential of mining complex network structures. The aim of this collection is to provide an insight into innovative methods to model, analyze and mine feature-rich networks inspired from different fields, incentivizing domain-driven approaches that can drive the design of novel network models.

Lead Guest Editor
Roberto Interdonato, CIRAD, France roberto.interdonato@cirad.fr

Guest editors:
Martin Atzmueller, Tilburg University, The Netherlands m.atzmuller@uvt.nl
Sabrina Gaito, University of Milano, Italy gaito@di.unimi.it
Rushed Kanawati, Paris 13 University, France rushed.kanawati@lipn.univ-paris13.fr
Christine Largeron, University of Lyon, France christine.largeron@univ-st-etienne.fr
Alessandra Sala, Nokia Bell Labs, Ireland alessandra.sala@nokia-bell-labs.com


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