Feature-rich networks: going beyond complex network topologies View Full Text


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Article Info

DATE

2019-12

AUTHORS

Roberto Interdonato, Martin Atzmueller, Sabrina Gaito, Rushed Kanawati, Christine Largeron, Alessandra Sala

ABSTRACT

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. More... »

PAGES

4

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s41109-019-0111-x

    DOI

    http://dx.doi.org/10.1007/s41109-019-0111-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111823558


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