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Modeling, analyzing and mining feature-rich networks

Pixabay, CC0 Creative Commons






The 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 [email protected]

Guest editors:
Martin Atzmueller, Tilburg University, The Netherlands [email protected]
Sabrina Gaito, University of Milano, Italy [email protected]
Rushed Kanawati, Paris 13 University, France [email protected]
Christine Largeron, University of Lyon, France [email protected]
Alessandra Sala, Nokia Bell Labs, Ireland [email protected]


  2. Local pattern mining on attributed networks is an important and interesting research area combining ideas from network analysis and data mining. In particular, local patterns on attributed networks allow both ...

    Authors: Martin Atzmueller, Henry Soldano, Guillaume Santini and Dominique Bouthinon
    Citation: Applied Network Science 2019 4:43
    Content type: Research Published on:

  3. Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In rea...

    Authors: Hana Khamfroush, Nathaniel Hudson, Samuel Iloo and Mahshid R. Naeini
    Citation: Applied Network Science 2019 4:40
    Content type: Research Published on:

  4. Applying closed pattern mining to attributed two-mode networks requires two conditions. First, as in two-mode networks there are two kinds of vertices, each described with a proper attribute set, we have to co...

    Authors: Henry Soldano, Guillaume Santini, Dominique Bouthinon, Sophie Bary and Emmanuel Lazega
    Citation: Applied Network Science 2019 4:37
    Content type: Research Published on:

  5. The advent of Online Social Networks (OSNs) has offered the opportunity to study the dynamics of information spread and influence propagation at a huge scale. Considerable research has focused on the social in...

    Authors: Luca Luceri, Torsten Braun and Silvia Giordano
    Citation: Applied Network Science 2019 4:34
    Content type: Research Published on:

  6. Community detection has proved to be extremely successful in a variety of domains. However, most of the algorithms used in practice assume networks are unchanging in time. This assumption is violated for many ...

    Authors: Thomas Magelinski and Kathleen M. Carley
    Citation: Applied Network Science 2019 4:25
    Content type: Research Published on:

  7. Knowledge graph will be usefull for the intelligent system. As the relationship prediction on the knowledge graph becomes accurate, construction of a knowledge graph and detection of erroneous information incl...

    Authors: Yohei Onuki, Tsuyoshi Murata, Shun Nukui, Seiya Inagi, Xule Qiu, Masao Watanabe and Hiroshi Okamoto
    Citation: Applied Network Science 2019 4:20
    Content type: Research Published on:

  9. A heterogeneous continuous time random walk is an analytical formalism for studying and modeling diffusion processes in heterogeneous structures on microscopic and macroscopic scales. In this paper we study bo...

    Authors: Liubov Tupikina and Denis S. Grebenkov
    Citation: Applied Network Science 2019 4:16
    Content type: Research Published on:

  13. We examine students’ representations of their conceptions of the interlinked nature of science history and general history, as well as cultural history. Such knowledge landscapes of the history of science are ...

    Authors: Henri Lommi and Ismo T. Koponen
    Citation: Applied Network Science 2019 4:6
    Content type: Research Published on:

    The Correction to this article has been published in Applied Network Science 2020 5:42

  14. 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 Attribut...

    Authors: Roberto Interdonato, Martin Atzmueller, Sabrina Gaito, Rushed Kanawati, Christine Largeron and Alessandra Sala
    Citation: Applied Network Science 2019 4:4
    Content type: Review Published on: