Feature-rich networks: Going beyond complex network topologies
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.
| Main Authors: | , , , , , |
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| Format: | article biblioteca |
| Language: | eng |
| Subjects: | U10 - Informatique, mathématiques et statistiques, analyse de réseau, topologie, modèle mathématique, analyse de données, http://aims.fao.org/aos/agrovoc/c_5144, http://aims.fao.org/aos/agrovoc/c_424c1a05, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_15962, |
| Online Access: | http://agritrop.cirad.fr/597769/ http://agritrop.cirad.fr/597769/1/41109_2019_111_OnlinePDF.pdf |
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