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.
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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, |
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dig-cirad-fr-5977692024-01-29T03:23:15Z http://agritrop.cirad.fr/597769/ http://agritrop.cirad.fr/597769/ Feature-rich networks: Going beyond complex network topologies. Interdonato Roberto, Atzmueller Martin, Gaito Sabrina, Kanawati Rushed, Largeron Christine, Sala Alessandra. 2019. Applied Network Science, 4:4, 13 p.https://doi.org/10.1007/s41109-019-0111-x <https://doi.org/10.1007/s41109-019-0111-x> Feature-rich networks: Going beyond complex network topologies Interdonato, Roberto Atzmueller, Martin Gaito, Sabrina Kanawati, Rushed Largeron, Christine Sala, Alessandra eng 2019 Applied Network Science 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 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/597769/1/41109_2019_111_OnlinePDF.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1007/s41109-019-0111-x 10.1007/s41109-019-0111-x info:eu-repo/semantics/altIdentifier/doi/10.1007/s41109-019-0111-x info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/s41109-019-0111-x |
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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 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 |
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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 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 Interdonato, Roberto Atzmueller, Martin Gaito, Sabrina Kanawati, Rushed Largeron, Christine Sala, Alessandra Feature-rich networks: Going beyond complex network topologies |
description |
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. |
format |
article |
topic_facet |
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 |
author |
Interdonato, Roberto Atzmueller, Martin Gaito, Sabrina Kanawati, Rushed Largeron, Christine Sala, Alessandra |
author_facet |
Interdonato, Roberto Atzmueller, Martin Gaito, Sabrina Kanawati, Rushed Largeron, Christine Sala, Alessandra |
author_sort |
Interdonato, Roberto |
title |
Feature-rich networks: Going beyond complex network topologies |
title_short |
Feature-rich networks: Going beyond complex network topologies |
title_full |
Feature-rich networks: Going beyond complex network topologies |
title_fullStr |
Feature-rich networks: Going beyond complex network topologies |
title_full_unstemmed |
Feature-rich networks: Going beyond complex network topologies |
title_sort |
feature-rich networks: going beyond complex network topologies |
url |
http://agritrop.cirad.fr/597769/ http://agritrop.cirad.fr/597769/1/41109_2019_111_OnlinePDF.pdf |
work_keys_str_mv |
AT interdonatoroberto featurerichnetworksgoingbeyondcomplexnetworktopologies AT atzmuellermartin featurerichnetworksgoingbeyondcomplexnetworktopologies AT gaitosabrina featurerichnetworksgoingbeyondcomplexnetworktopologies AT kanawatirushed featurerichnetworksgoingbeyondcomplexnetworktopologies AT largeronchristine featurerichnetworksgoingbeyondcomplexnetworktopologies AT salaalessandra featurerichnetworksgoingbeyondcomplexnetworktopologies |
_version_ |
1792500119361290240 |