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Applications of Temporal Graph Metrics to Real-World Networks

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Temporal Networks

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.

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Notes

  1. 1.

    The static degree centrality is defined as the number of edges connected to a node i, normalised by the total possible neighbour nodes (n − 1) [56].

  2. 2.

    The AUC is commonly used in epidemiology and medical trials [21].

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Acknowledgements

This work was funded in part through EPSRC Project MOLTEN (EP/I017321/1) and the EU LASAGNE Project, Contract No.318132 (STREP).

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Authors and Affiliations

  1. Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK

    John Tang, Ilias Leontiadis, Salvatore Scellato, Vincenzo Nicosia & Cecilia Mascolo

  2. Laboratorio sui Sistemi Complessi, Scuola Superiore di Catania, Via Valdisavoia 9, 95123, Catania, Italy

    Vincenzo Nicosia & Vito Latora

  3. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Mirco Musolesi

  4. School of Mathematical Sciences, Queen Mary, University of London, E1 4NS, London, UK

    Vito Latora

  5. Dipartimento di Fisica e Astronomia and INFN, Universitá di Catania and INFN, Via S. Sofia 64, 95123, Catania, Italy

    Vito Latora

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  1. John Tang
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  2. Ilias Leontiadis
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  3. Salvatore Scellato
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  4. Vincenzo Nicosia
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  6. Mirco Musolesi
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  7. Vito Latora
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Correspondence to Mirco Musolesi .

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Editors and Affiliations

  1. Ice Lab, Department of Physics, Umeå University, Ice Lab, 3rd floor, NA wing, Naturvetarhuset, Umeå, 901 87, Sweden

    Petter Holme

  2. and Computational Sciences (BECS), Department of Biomedical Engineering, Aalto University, Rakentajanaukio, Otaniemi campus 2, Aalto, 00076, Finland

    Jari Saramäki

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Tang, J. et al. (2013). Applications of Temporal Graph Metrics to Real-World Networks. In: Holme, P., Saramäki, J. (eds) Temporal Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36461-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-36461-7_7

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