Graph Metrics for Temporal Networks

Part of the Understanding Complex Systems book series (UCS)


Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node–node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.


Betweenness Centrality Static Graph Modularity Function Closeness Centrality Temporal Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded in part through EPSRC Project MOLTEN (EP/I017321/1).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK
  2. 2.Laboratorio sui Sistemi ComplessiScuola Superiore di CataniaCataniaItaly
  3. 3.School of Computer ScienceUniversity of BirminghamBirminghamUK
  4. 4.Dipartimento di Matematica e InformaticaUniversitá di CataniaCataniaItaly
  5. 5.School of Mathematical SciencesQueen Mary, University of LondonLondonUK
  6. 6.Dipartimento di Fisica e Astronomia and INFNUniversitá di CataniaCataniaItaly

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